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Introduction
Generative AI’s Great Leap Forward?
The Real-World Use Cases for Predictive and Generative AI
The Business Value of AI
The Future of Generative and Predictive AI

Encord Blog

Generative AI is overrated, long live old-school AI!

March 15, 2023

5 mins

Back to Blogs

Contents

Introduction
Generative AI’s Great Leap Forward?
The Real-World Use Cases for Predictive and Generative AI
The Business Value of AI
The Future of Generative and Predictive AI

Written by

Ulrik Stig Hansen

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TLDR; Don't be dazzled by generative AI's creative charm! Predictive AI, though less flashy, remains crucial for solving real-world challenges and unleashing AI's true potential. By merging the powers of both AI types and closing the prototype-to-production gap, we'll accelerate the AI revolution and transform our world. Keep an eye on both these AI stars to witness the future unfold.

Introduction

Throughout 2022, generative AI captured the public’s imagination. Now that GPT-4 is out, the hype is poised to reach new heights.

With the late 2022 release(s) of Stable Diffusion, Dall-E2, and ChatGPT, people could engage with AI first-hand, watching with awe as seemingly intelligent systems created art, composed songs, penned poetry, and wrote passable college essays.

Only a few months later, some investors have become only interested in companies building generative AI, relegating those working on predictive models to “old school” AI.

However, generative AI alone won’t fulfill the promise of the AI revolution. The sci-fi future that many people anticipate accompanying the widespread adoption of AI depends on the success of predictive models. Self-driving cars, robotic attendants, personalized healthcare, and many other innovations hinge on perfecting “old school” AI.

Not your average giraffe

Not your average giraffe

Generative AI’s Great Leap Forward?

Predictive and generative AI is designed to perform different tasks.

Predictive models infer information about different data points to make decisions. Is this an image of a dog or a cat? Is this tumor benign or malignant? A human supervises the model’s training, telling whether its outputs are correct. Based on the training data it encounters, the model learns to respond to different scenarios differently.

Generative models produce new data points based on what they learn from their training data. These models typically train in an unsupervised manner, analyzing the data without human input and drawing conclusions.

For years, generative models had the more complex tasks, such as trying to learn to generate photorealistic images or create textual information that answers questions accurately, and progress moved slowly.

Then, an increase in the availability of computing power enabled machine learning teams to build foundation models– massive unsupervised models that train vast amounts of data (sometimes all the data available on the internet). Over the past couple of years, ML engineers have calibrated these generative foundation models– feeding them subsets of annotated data to target outputs for specific objectives– so that they can be used for practical applications.

ChatGPT is a good example. It’s built on a version of GPT-n, foundation models trained on vast amounts of unlabelled data. To create ChatGPT-3, OpenAI hired 6,000 annotators to label an appropriate subset of data. Its ML engineers then used that data to fine-tune the model to teach it to generate specific information.

With these sorts of fine-tuning methods, generative models have begun to create previously incapable outputs. The result has been a swift proliferation of functional generative models. This sudden expansion makes it appear that generative AI has leapfrogged the performance of existing predictive AI systems.

The newly released GPT-4 exhibits human-level performance on a variety common professional and academic exams.

The newly released GPT-4 exhibits human-level performance on a variety of common and professional academic exams. Source: OpenAI GPT-4 Technical Report

Appearances, however, can be deceiving.

The Real-World Use Cases for Predictive and Generative AI

Regarding current real-world use cases for these models, people use generative and predictive AI differently.

Predictive AI has primarily been used to free up people’s time by automating human processes to perform at very high levels of accuracy and with minimal human oversight.

Most of the current use cases for generative AI still require human oversight. For instance, these models have been used to draft documents and co-author code, but humans are still “in the loop” reviewing and editing the outputs. In contrast, the current iteration of generative AI is primarily used to augment rather than replace human workloads.

Currently, generative models haven’t yet been applied to high-stakes use cases, so whether they have significant error rates doesn’t matter much. Their current applications, such as creating art or writing essays, don’t carry many risks. What harm is done if a generative model produces an image of a woman with eyes too blue to be realistic?

... blue contacts, anyone?

... blue contacts, anyone?

On the other hand, many of the use cases for predictive AI carry risks that can have a very real impact on people’s lives. As a result, these models must achieve high-performance benchmarks before they’re released into the wild. Whereas a marketer might use a generative model to draft a blog post that’s 80 percent as good as the one they would have written themselves, no hospital would use a medical diagnostic system that predicts with only 80 percent accuracy.

While on the surface, it may appear that generative models have taken a giant leap forward in terms of performance when compared to their predictive counterparts, all things equal, most predictive models are required to perform at a higher accuracy level because their use cases demand it.

Even lower-stakes predictive AI models like email filtering must meet high-performance thresholds. If a spam email lands in a user’s inbox, it’s not the end of the world, but if a critical email gets filtered directly to spam, the results could be severe.

The capacity at which generative AI can currently perform is far from the threshold required to make the leap into production for high-risk applications. Using a generative text-to-image model with likely error rates to make art may have enthralled the general public. However, no medical publishing company would use that same model to generate images of benign and malignant tumors to teach medical students. The stakes are too high.

The Business Value of AI

While predictive AI may have recently taken a backseat in terms of media coverage, in the near- to medium-term, these systems are still likely to deliver the most significant value for business and society.

Although generative AI creates new data about the world, it’s less helpful in solving problems on existing data. Most urgent large-scale problems humans must solve necessitates making inferences about and decisions based on real-world data.

Predictive AI systems can read documents, control temperature, analyze weather patterns, evaluate medical images, assess property damage, and more. They can generate immense business value by automating vast data and document processing. Financial institutions, for instance, use predictive AI to review and categorize millions of transactions daily, saving employees from these time and labor-intensive tasks.

Scale your annotation workflows and power your model performance with data-driven insights

However, many of the real-world applications for predictive AI that have the potential to transform our day-to-day lives depend on perfecting existing models so that they achieve the performance benchmarks required to enter production. Closing the proof-of-concept to production performance gap is the most challenging part of model development, but it’s essential if AI systems are to reach their potential.

The Future of Generative and Predictive AI

So has generative AI been overhyped?

Not exactly. Having generative models capable of delivering value is an exciting development. For the first time, people can interact with AI systems that don’t just automate but create– an activity of which only humans were previously capable.

Nonetheless, the current performance metrics for generative AI aren’t as well defined as those for predictive AI, and measuring the accuracy of a generative model is complex. If the technology is going to one day be used for practical applications– such as writing a textbook– it will ultimately need to have performance requirements similar to that of predictive models. Likewise, predictive and generative AI will merge eventually. Mimicking human intelligence and performance requires having one system that is both predictive and generative. That system will need to perform both of these functions at high levels of accuracy.

Factuality evaluations for GPT-4 are still around or below 80% on a broad set of categories - not yet usable for high risk use cases

Factuality evaluations for GPT-4 are still around or below 80% on a broad set of categories - not yet usable for high-risk use cases. Source: OpenAI GPT-4 Technical Report

In the meantime, however, if we want to accelerate the AI revolution, we shouldn’t abandon “old school AI” for its flashier cousin. Instead, we must focus on perfecting predictive AI systems and putting resources into closing the proof-of-concept-to-production-gap for predictive models.

If we don’t, then ten years from now, we might be able to create a symphony from text-to-sound models, but we’ll still be driving ourselves.

Courtesy of Stable Diffusion

Courtesy of Stable Diffusion

Ulrik Stig Hansen is Co-Founder & President of Encord. He holds an M.S. in Computer Science from Imperial College London.

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Ulrik Stig Hansen

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May 25 2023

5 M

sampleImage_data-refinement-guide-computer-vision

machine learning

Data Refinement Strategies for Computer Vision

Data refinement strategies for computer vision are integral for improving the data quality used to train machine learning-based models. Computer vision is becoming increasingly mission-critical across dozens of sectors, from facial recognition on social media platforms to self-driving cars. However, developing effective computer vision models is a challenging task. One of the key challenges in computer vision is dealing with large amounts of data and ensuring that the data is of high quality. This is where data refinement strategies come in. In this blog post, we will explore the different data refinement strategies in computer vision and how they can be used to improve the performance of machine learning models. We will also discuss the tools and techniques for creating effective data refinement strategies. Model-centric vs. Data-centric Computer Vision In computer vision, there are two paradigms: model-centric and data-centric. Both of these paradigms share a common goal of improving the performance of machine learning models, but they differ in their approach to achieving this objective. Model-centric computer vision relies on developing, experimenting, and improving complex machine learning (ML) models to accomplish tasks like object detection, image recognition, and semantic segmentation. Here the datasets are considered static, and changes are added to the model (architecture, hyperparameters, loss functions, etc.) to improve performance. Data-centric computer vision: In recent years, there has been a growing focus on data-centric computer vision as researchers and engineers recognize the significance of high-quality data in building effective ML, AI, and CV models. The models, their hyperparameters, etc., play only a minor role in the machine learning pipeline. On the other hand, data-centric computer vision prioritizes the quality and quantity of data used to train these models. Why Do We Need Data Refinement Strategies? Data refinement strategies are crucial in improving the quality of data and labels used to train machine learning models, as the quality of data and labels directly impacts the model's performance. Here are some ways data refinement strategies can help: Identifying Outliers Outliers are data points that do not follow the typical distribution of the dataset. Outliers can cause the model to learn incorrect patterns, leading to poor performance. By removing outliers, the model can focus on learning the correct patterns, leading to better performance. Identifying and Removing Noisy Data Noisy data refers to data that contains irrelevant or misleading information, such as duplicates or low-quality images. These data points can cause models to learn incorrect patterns, leading to inaccurate predictions. Identifying and Correcting Label Errors Label errors occur when data points are incorrectly labeled or labeled inconsistently, leading to misclassifying objects in images or videos. Correcting label errors ensures that the model receives accurate information during training, improving its ability to predict and classify objects accurately. 💡Read how to find and fix label errors Assisting in Model Performance Optimization and Debugging Data refinement strategies help preserve and debug the best-performing model by correcting incorrect labels that could affect the model’s performance evaluation metrics. You can get a more accurate and effective model by improving the data quality used to train the model. 💡You can try the different refinement strategies with Encord Active on GitHub today Common Data Refinement Strategies in Computer Vision Computer vision has made great strides in recent years, with applications across industries from healthcare to autonomous vehicles. However, the data quality used to train machine learning models is critical to their success. There are several common data refinement strategies used in computer vision. These strategies are designed to improve the data quality used to train machine learning models. The once we will cover today are: Smart data sampling Improving data quality Improving label quality Finding model failure modes Active learning Semi-supervised learning (SSL) 💡With Encord Active, you can visualize image embeddings, show images from a particular cluster, and export them for relabeling. Smart Data Sampling It involves identifying relevant data and removing irrelevant data. Rather than selecting data randomly or without regard to specific characteristics, smart data sampling involves using a systematic approach to choose data points most representative of the entire dataset. For example, if you train a model to recognize cars, we would want to select the cars in the street-view data. The goal of smart data sampling is to reduce the amount of data needed for training without sacrificing model accuracy. This technique can be advantageous when dealing with large datasets requiring significant computational resources and processing time. For example, image embeddings can be used for smart data sampling by clustering similar images based on their embeddings. These clusters can be used to filter out the images which have duplicates in the dataset and eliminate them. This reduces the amount of data needed for training while ensuring that the dataset is representative of the overall dataset. K-means and hierarchical clustering are two approaches to using image embeddings for smart data sampling. Improving Data Quality Improving the quality of data in the data refinement stage of machine learning, various techniques can be used, including data cleaning, data augmentation, balancing the dataset, and data normalization. These techniques help to ensure that the model is accurate, generalizes well on unseen data, and is not biased towards a particular class or category. 💡Read this post next if you want to find out how to improve the quality of labeled data Improving Label Quality Label errors can occur when the data is mislabeled or when the labels are inconsistent. You also need to ensure that all classes in the dataset are adequately represented to avoid biases and improve the model's performance in classifying minority classes. Improving label quality ensures that computer vision algorithms accurately identify and classify objects in images and videos. To improve label quality, data annotation teams can use complex ontological structures that clearly define objects within images and videos. You can also use AI-assisted labeling tools to increase efficiency and reduce errors, identify and correct poorly labeled data through expert review workflows and quality assurance systems, and improve annotator management to ensure consistent and high-quality work. Organizations can achieve higher accuracy scores and produce more reliable outcomes for their computer vision projects by continually assessing and improving label quality. Finding Model Failure Modes Machine learning models can fail in different ways. For example, the model may struggle to recognize certain types of objects or may have difficulty with images taken from certain angles. Finding model failure modes is a critical first step in the testing process of any machine learning model. Thoroughly testing a model requires considering potential failure modes, such as edge cases and outliers, that may impact its performance in real-world scenarios. These scenarios may include factors that could impact the model's performance, such as changing lighting conditions, unique perspectives, or environmental variations. By identifying scenarios where a model might fail, one can develop test cases that evaluate the model's ability to handle these scenarios effectively. It's important to note that identifying model failure modes is not a one-time process and should be revisited throughout the development and deployment of a model. As new scenarios arise, it may be necessary to add new test cases to ensure that a model continues to perform effectively in all possible scenarios. 💡Read more to find out how to evaluate ML models using model test cases. Active Learning Active learning is another strategy to improve your data and your model performance. Active learning involves iteratively selecting the most informative data samples for annotation by human annotators, thereby reducing the annotation effort and cost. This strategy is advantageous when large datasets need to be annotated, as it allows for more efficient use of resources. By selecting the most valuable samples for annotation, active learning can help improve the quality of the dataset and the accuracy of the resulting machine learning models. To implement active learning in computer vision, you first train a model on a small subset of the available data. The model then selects the most informative data points for annotation by a human annotator, who labels the data and adds it to the training set. This process continues iteratively, with the model becoming more accurate as it learns from the newly annotated data. There are several benefits to using active learning in computer vision: It reduces the amount of data that needs to be annotated, saving time and reducing costs. It improves the accuracy of the machine learning model by focusing on the most informative data points. Active learning enables the model to adapt to changes in the data distribution over time, ensuring that it remains accurate and up-to-date. 💡Read this post to learn about the role of active learning in computer vision Semi-Supervised Learning In semi-supervised learning (SSL), a combination of labeled and unlabeled data is used to train the model. The model leverages a large amount of unlabeled data to learn the underlying distribution and subsequently utilizes the labeled data to refine its hyperparameters and enhance the model’s overall performance. SSL can be particularly useful when obtaining labeled data is expensive or time-consuming (see the figure below). If you want to learn more about the current state of semi-supervised learning, you can read A Cookbook for Self-supervised Learning co-authored by Yann LeCun. 💡Note: The data refinement strategies are inclusive of one another. They can be used in combination. For example, active learning and semi-supervised learning can be used together. What Do You Need to Create Data Refinement Strategies? To create effective data refinement strategies, you will need: data, labels, model predictions, intuition, and the right tooling: Data and Labels A large amount of high-quality data is required to train the machine learning model accurately. The data must be clean, relevant, and representative of the target population. Labels are used to identify the objects in the images, and these labels must be accurate and consistent. It is critical to develop a clear labeling schema that is comprehensive and allows for the identification of all relevant features. Model Predictions Evaluating the performance of a machine learning model is necessary to identify areas that require improvement. Model predictions provide valuable insights into the accuracy and robustness of the model. Moreover, your model predictions combined with your model embedding are very useful when detecting detect data and labeling outliers and errors. Intuition Developing effective data refinement strategies requires a deep understanding of the data and the machine learning model. This understanding comes from experience and familiarity with the data and the technology. Expertise in your problem is critical for identifying relevant features and ensuring that the model effectively solves the problem. Tools A range of tools can be used to create effective data refinement strategies. For example, the Encord Active platform provides a range of metrics-based data and label quality improvement tools. These include labeling, evaluation, active learning, and experiment-tracking tools. Primary Methods for Data Refinement There are three primary methodologies for data refinement in computer vision. Refinement by Image This approach involves a meticulous manual review and selection of individual images to be included in the training dataset for the machine learning model. Each image is carefully analyzed for its suitability and relevance before being incorporated into the dataset. Although this method can yield highly accurate and well-curated data, it is often labor-intensive and costly, making it less feasible for large-scale projects or when resources are limited. Refinement by Class In this method, data refinement is based on the class or category of objects present in the images. The process involves selecting and refining data labels associated with specific classes or categories, ensuring that the machine learning model is trained with accurate and relevant information. This approach allows for a more targeted refinement process, focusing on the specific object classes that are of interest in the computer vision task. This method can be more efficient than the image-by-image approach, as it narrows the refinement process to relevant object classes. Refinement by Quality Metrics This methodology focuses on selecting and enhancing data according to predefined quality metrics. These metrics may include factors such as image resolution, clarity of labels, or the perspective from which the images are taken. By establishing and adhering to specific quality criteria, this approach ensures that only high-quality images are included in the training dataset, thus reducing the influence of low-quality images on the model's performance. This method can help streamline the refinement process and improve the overall effectiveness of the machine learning model. Alternatively, this process can be automated with active learning tools and pre-trained models. We will cover this in the next section. Practical Example of Data Refinement Encord Active is an open-source active learning toolkit designed to facilitate identifying and correcting label errors in computer vision datasets. With its user-friendly interface and a variety of visualization options, Encord Active streamlines the process of investigating and understanding failure modes in computer vision models, allowing users to optimize their datasets efficiently. To install Encord Active using pip, we need to run a command: pip install encord-active For more installation information, please read the documentation. You can import a COCO project to Encord Active with a single-line command: encord-active import project --coco -i ./images_folder -a ./annotations.json Executing this command will create a local Encord Active project and pre-calculate all associated quality metrics for your data. Quality metrics are supplementary parameters for your data, labels, and models, providing semantically meaningful and relevant indices for these elements. Encord Active offers a variety of pre-computed metrics that can be incorporated into your projects. Additionally, the platform allows you to create custom metrics tailored to your specific needs. A local Encord Active instance will be available upon successfully importing the project, enabling you to examine and analyze your dataset thoroughly. To open the imported project, just run the command: encord-active visualize Filtering Data and Label Outliers For data analysis, begin by navigating to the Data Quality → Summary tab. Here, you can examine the distribution of samples about various image-level metrics, providing valuable insights into the dataset's characteristics. Data Quality Summary page of Encord Active Using the summary provided by Encord Active, you can identify properties exhibiting significant outliers, such as extreme green values, which can be crucial for assessing the dataset's quality. The platform offers features that enable you to either distribute the outliers evenly when partitioning the data into training, validation, and test datasets or to remove the outliers entirely if desired. For label analysis, navigate to the Label Quality → Summary tab. Here, you can examine your dataset's quality of label-level metrics. Below the label quality summary, you can find the label distribution of your dataset On both Summary tabs, you can scroll down to get detailed information and visualization of the detected outliers and which metric to focus on. Image outliers detected based on brightness quality metric Once the outlier type has been identified, you would want to fix it. You can go to Data Quality → Explorer and filter images by the chosen metric value (brightness). Next, you can tag these images with the tag "high brightness" and download the data labels with added tags or directly send data for relabeling in Encord Annotate. Images filtered by brightness values and added the tag "high brightness" Finding Label Errors with a Pre-Trained Model and Encord Active As your computer vision projects advance, you can utilize a trained model to detect label errors in your data annotation pipeline. To achieve this, follow a straightforward process: Use a pre-trained model on newly annotated samples to generate model predictions. Import the model predictions into Encord Active. Click here to find further instructions. encord-active import predictions --coco results.json Overlay model predictions and ground truth labels for visualization within the Encord Active platform. Sort by high-confidence false-positive predictions and compare them against the ground truth labels. Once discrepancies are identified, flag the incorrect or missing labels and forward them for re-labeling using Encord Annotate. Model Quality page of Encord Active To ensure the integrity of the process, it is crucial that the computer vision model employed to generate predictions has not been trained on the newly annotated samples under investigation. Conclusion In summary, the success of machine learning models in computer vision heavily relies on the quality of data used to train them. Data refinement strategies, such as active learning, smart data sampling, improving data and label quality, and finding model failure modes, are crucial in ensuring that the models produce reliable and accurate results. These strategies require high-quality data, accurate and consistent labels, and a deep understanding of the data and the technology. Using effective data refinement strategies, you can achieve higher model accuracy and produce more reliable outcomes for your computer vision model. It is essential to continually assess and refine data quality throughout the development and deployment of machine learning models to ensure that they remain accurate and up-to-date in real-world scenarios. Ready to improve the data refinement of your CV models? Sign-up for an Encord Free Trial: The Active Learning Platform for Computer Vision, used by the world’s leading computer vision teams. AI-assisted labeling, model training & diagnostics, find & fix dataset errors and biases, all in one collaborative active learning platform, to get to production AI faster. Try Encord for Free Today. Want to stay updated? Follow us on Twitter and LinkedIn for more content on computer vision, training data, and active learning.

May 11 2023

5 M

machine learning

machine learning

Best Datasets for Computer Vision [Industry breakdown]

Image and video dataset annotations are crucial for training machine learning (ML) models for computer vision. One of the challenges is finding the best possible, highest-quality datasets to annotate to start training your model. The good news is, there are dozens of free and open-source image and video-based public datasets for machine learning models you can use. Whether you use a public dataset depends on your project goals and the problem you are trying to solve. Your project might need proprietary in-house data, or commercial specialist datasets to solve a particular problem. However, there are numerous use cases where public, open-source datasets that have already been annotated are ideal for training computer vision models. Organizations in dozens of sectors — insurance, healthcare, smart cities, retail, sports, and many others — are already using public datasets to train ML models and solve image and video-related big data challenges. ‍In this post, we take a closer look at the best open-source and free datasets for machine learning models across numerous sectors, such as healthcare and insurance, and review the actions you should take once you have found a suitable dataset for your project. What is a Dataset for Machine Learning (ML)? Datasets used to train machine learning (ML), artificial intelligence (AI), and other algorithmically-based models can be anything from spreadsheets to a repository of videos. A dataset is simply a way of saying that you have a collection of data, regardless of the format. In the case of computer vision (CV) and ML models, datasets usually contain thousands of images or videos or both. Training an ML-based model to solve a particular problem means ensuring you’ve picked the right type of images or videos, in the most suitable format, with the highest quality of annotations and labels possible to produce the results you want. Poor quality data — or thousands of irrelevant images or videos within a dataset — will generate negative project outcomes. ML projects often work on tight deadlines. Using a public dataset saves time, as there are far fewer data cleansing tasks, so it’s a shortcut to having a proof of concept (POC) project up and running. However, you need to ensure the images or videos contained within any datasets you are going to use are relevant to your project goals. You need to ensure the annotations, labels, and metadata are high quality, with sufficient modalities, and object types. It’s also important that there is a sufficient range of images or videos to reduce bias and produce the answers you need once this data is fed into an ML model. Datasets should also contain a wide range of images or videos under different conditions, e.g. light, dark, day, night, shadows, etc. Higher quality data produces better ML and CV project outcomes. What is a Classification Dataset? A classification dataset is a dataset that’s used to categorize a specific object amongst a range of options. With image classification, the image is the input and the output is a label applied to an object or objects within that image. Image classification datasets are used to train ML-based or other algorithmically-generated models to identify with a high degree of accuracy the object(s) you are looking for. For example, if you’re looking for images that identify a specific make and model of a car, you need a dataset with enough images containing that car, alongside hundreds or thousands of images of cars that are not that make or model. In this scenario, it would be a waste of time to show ML models thousands of images of tractors when you need it to identify a specific type of car. Hence the importance of picking the right dataset for your ML project. Should I use Synthetic Data to Train my Machine Learning and Computer Models? ML, AI, and computer vision models are data intensive. Even when you use micro-models to train them, when it comes to solving problems at scale, you need A LOT of data. But in some use cases, that simply isn’t possible. How many images and videos of things that don’t happen very often or even exist anymore have made their way into datasets? The answer is ‘not many’. Not enough to train an ML-based model accurately, without bias, and dozens of other problems occur when a model doesn’t have enough data to learn from. In edge cases such as this, ML teams need synthetic data. For more information, here’s An Introduction to Synthetic Training Data Synthetic data solves the problems of difficult-to-find datasets, such as pictures of pedestrians' shadows, rare diseases, or car crashes. Synthetic data is manufactured images and videos, useful when edge cases need hundreds of thousands of images or videos, and yet only a few thousand exist in reality. Computer-generated images (CGI), 3D games engines — such as Unity and Unreal — and Generative adversarial networks (GANs) are ideal solutions for this problem. Of course, the tools used to create these images or videos depend on your budget and how much time you have to create synthetic data. You can also buy custom or ready-made synthetic data that should fill your imaging dataset gaps and help train an ML model more effectively. ‍Now let’s take a look at the dozens of options we’ve found for sourcing open-source public datasets for machine learning. ‍ Where Can I Find Datasets For Machine Learning? To make these dataset sources easier to find, we have broken this list down according to sectors: Insurance Sports SAR (Synthetic Aperture Radar) Smart cities and autonomous vehicles Retailers and manufacturers Medical and healthcare Open dataset aggregators Let’s dive in . . . ‍Insurance Machine Learning Datasets ‍Car Damage Assessment Dataset On Kaggle, one of the best places to find high-quality datasets (both balanced and imbalanced), there are dozens of image and video files available to download for free. Kaggle is a data science community with hundreds of resources for professionals in this field, including open-source datasets, making it an open dataset aggregator. One of these datasets is ideal for car insurance data analysts and data scientists. It’s a folder of 1,500 unique RGB images (224 x 224 pixels) split into a training and validation subset. It contains classifications such as broken headlamps, glass shatter, dumper dent, and all of the most common car damage categories. Car Damage Assessment Dataset Sports Analytics Datasets KTH (KTH Royal Institute of Technology, Stockholm) Multiview Football Dataset I & II This dataset contains thousands of images of football players during an Allsvenskan League professional game. It includes one dataset with images with ground truth pose in 2D and the other dataset with ground truth pose in both 2D and 3D. Datasets such as this can help train computer vision models on Human Pose Estimation (HPE) techniques. It includes around 7,000 images, dozens of annotated joints and players, an orthographic camera matrix for each frame, and images calibrated and synchronized. The open-source provider of this dataset, the KTH Royal Institute of Technology, says it can’t be used for commercial purposes, only academia and research. OpenTTGames Dataset The OpenTTGames Dataset was created for the evaluation of computer vision tasks for the game of table tennis. It is designed to help ML scientists and data ops managers with table tennis projects evaluate the following: “ball detection, semantic segmentation of humans, table and scoreboard and fast in-game events spotting.” It includes 5 videos between 10 and 25 minutes in length, fully annotated, markup files provided, and 7 short testing/training annotated videos. SAR (Synthetic Aperture Radar), Overhead, Satellite Datasets Open-source image datasets are one of the best places for organizations that need overhead satellite images, such as those captured by synthetic aperture radar (SAR). Here are some of the most valuable sources of annotated satellite imagery datasets: ‍xView xView is one of the largest publicly available datasets of overhead and satellite imagery. It contains images from complex scenes around the world, annotated using bounding boxes. xView includes over 1 million object instances, 60 classes, and a 0.3 meter resolution. ‍xView3‍ xView3 is a dataset that contains approximately 1000 scenes from maritime regions of interest. Each scene consists of two SAR images (VV, VH); and each scene also includes five ancillary images: bathymetry, wind speed, wind direction, wind quality, and land/ice mask. The xView3 maritime image dataset was sourced from synthetic aperture radar (SAR) imagery from the Copernicus Sentinel-1 mission of the European Space Agency (ESA), taken from two polar-orbiting satellites, with images taken in every weather conditions, every day and night. The resolution is 20 meters, and it’s a useful dataset for detecting ships against sea clutter, while also being useful for identifying near-shore and sea/ocean surface features. ‍EU Copernicus Constellation‍ Copernicus is a European Union (EU) funded project, whereby a constellation of satellites are taking thousands of pictures every day, building a vast database of marine, land, and air-based images. Consequently, Copernicus is one of the largest image-based dataset creators in the world, producing 16 terabytes of data per day. The majority of images Copernicus produces are “made available and accessible to any citizen, and any organization around the world on a free, full, and open basis.” Smart Cities & Autonomous Vehicles ML Datasets ‍BDD100K Berkeley DeepDrive is a diverse and extensive dataset for heterogeneous multitask learning. It contains over 100,000 driving videos collected from 50,000 car journeys. Each is 40-seconds long, 30fps, with over 100 million frames in total. Videos in this dataset include city streets, residential areas, highways, and every type of weather condition. The dataset includes: “lane detection, object detection, semantic segmentation, instance segmentation, panoptic segmentation, multi-object tracking, segmentation tracking, and more.” BDD100K Dataset ‍The KITTI Vision Benchmark Suite The KITTI benchmark dataset contains a suite of vision tasks built using and for autonomous driving platforms. The full benchmark contains many tasks such as stereo, optical flow, visual odometry, and numerous others. This dataset contains the object detection dataset, including the monocular images and bounding boxes. The dataset contains 7481 training images annotated with 3D bounding boxes. This project was developed by the Karlsruhe Institute of Technology and Toyota Technological Institute in Chicago, with a car driving around a mid-size city equipped with several cameras and sensors. Datasets for Retailers & Manufacturers ‍RPC-Dataset Project ‍ The RPC dataset project is a large-scale and fine-grained retail product checkout dataset, one of the most extensive datasets in terms of both product image quantity and product categories. The dataset was created to solve the problem of images aligning with product databases at automated checkouts (ACOs). The test dataset contains 24,000 images, and there are a further 6,000 validation images, plus 53,739 training dataset images. Within the images are several layers of annotations, labels, and over 300,000 objects. ‍Zalando Fashion MNIST (on Kaggle) The Zalando Fashion MNIST dataset of Zalando's clothing images—consists of a training set of 60,000 examples, and a test set of 10,000 annotated and labeled fashion images. Each example is a 28x28 grayscale image, associated with a label from 10 classes. MNIST databases are popular amongst the AI/ML and computer vision community to validate training datasets. In this case, it’s useful for CV projects that need to use fashion image datasets. For even more fashion and retail datasets, here’s a list of 12 free retail datasets for computer vision models. ‍Medical Imaging Datasets ‍ The Cancer Imaging Archive (TCIA)‍ The Cancer Imaging Archive (TCIA) is a service that de-identifies cancer images (by removing patient data) and makes them available for free download. Hospitals and other medical providers can upload data to this public research project. With TCIA, healthcare companies and researchers can access thousands of cancer datasets in several ways, including through a portal and an API. ‍NIH Chest X-Rays (on Kaggle) The National Institute of Health Chest X-Ray Dataset contains 112,000 chest X-ray images from over 30,000 patients. Natural Language Processing (NLP) was used to annotate text-mine disease classifications from the associated radiological reports, with a 90% accuracy rating. NIH Chest X-Rays Dataset And finally, here’s a list of popular open dataset aggregators for machine learning models. ‍Open Dataset Aggregators ‍Kaggle‍ Kaggle is a community of ML practitioners and students, containing thousands of open-source datasets from dozens of sectors and verticals. You will need to drill down to search for an image or video-specific datasets that you can use for your project, depending on your particular project goals. It’s a valuable resource and research tool for the ML and computer vision community. ‍OpenML‍ OpenML is an open platform for sharing and finding machine learning, image, and video-based datasets. It’s open and free to use, for anyone and any purpose. Every dataset on the platform is uniformly formatted with rich metadata included, suitable to be uploaded into any tool, and to train any kind of ML, AI, or Computer Vision model. And there we go, an extensive list of open-source image and video datasets! Free for anyone and any organization to use, for almost any purpose, including commercial, in most cases. We hope you have found it useful! Ready to automate and improve the quality of your data annotations? Sign-up for an Encord Free Trial: The Active Learning Platform for Computer Vision, used by the world’s leading computer vision teams. AI-assisted labeling, model training & diagnostics, find & fix dataset errors and biases, all in one collaborative active learning platform, to get to production AI faster. Try Encord for Free Today. Want to stay updated? Follow us on Twitter and LinkedIn for more content on computer vision, training data, and active learning. Join our Discord channel to chat and connect.

Nov 10 2022

10 M

sampleImage_image-segmentation-for-computer-vision-best-practice-guide

machine learning

Guide to Image Segmentation in Computer Vision: Best Practices

Image segmentation is a crucial task in computer vision, where the goal is to divide an image into different meaningful and distinguishable regions or objects. It is a fundamental task in various applications such as object recognition, tracking, and detection, medical imaging, and robotics. Many techniques are available for image segmentation, ranging from traditional methods to deep learning-based approaches. With the advent of deep learning, the accuracy and efficiency of image segmentation have improved significantly. In this guide, we will discuss the basics of image segmentation, including different types of segmentation, applications, and various techniques used for image segmentation, including traditional, deep learning, and foundation model techniques. We will also cover evaluation metrics and datasets for evaluating image segmentation algorithms and future directions in image segmentation. By the end of this guide, you will have a better understanding of image segmentation, its applications, and the various techniques used for segmenting images. This guide is for you if you are a data scientist, machine learning engineer or your team is considering using image segmentation as part of an artificial intelligence computer vision project. What is Image Segmentation? Image segmentation is the process of dividing an image into multiple meaningful and homogeneous regions or objects based on their inherent characteristics, such as color, texture, shape, or brightness. Image segmentation aims to simplify and/or change the representation of an image into something more meaningful and easier to analyze. Here, each pixel is labeled. All the pixels belonging to the same category have a common label assigned to them. The task of segmentation can further be done in two ways: Similarity: As the name suggests, the segments are formed by detecting similarity between image pixels. It is often done by thresholding (see below for more on thresholding). Machine learning algorithms (such as clustering) are based on this type of approach for image segmentation. Discontinuity: Here, the segments are formed based on the change of pixel intensity values within the image. This strategy is used by line, point, and edge detection techniques to obtain intermediate segmentation results that may be processed to obtain the final segmented image. Types of Segmentation Image segmentation modes are divided into three categories based on the amount and type of information that should be extracted from the image: Instance, semantic, and panoptic. Let’s look at these various modes of image segmentation methods. Also, to understand the three modes of image segmentation, it would be more convenient to know more about objects and backgrounds. Objects are the identifiable entities in an image that can be distinguished from each other by assigning unique IDs, while the background refers to parts of the image that cannot be counted, such as the sky, water bodies, and other similar elements. By distinguishing between objects and backgrounds, it becomes easier to understand the different modes of image segmentation and their respective applications. Instance Segmentation Instance segmentation is a type of image segmentation that involves detecting and segmenting each object in an image. It is similar to object detection but with the added task of segmenting the object’s boundaries. The algorithm has no idea of the class of the region, but it separates overlapping objects. Instance segmentation is useful in applications where individual objects need to be identified and tracked. Instance segmentation Semantic Segmentation Semantic segmentation is a type of image segmentation that involves labeling each pixel in an image with a corresponding class label with no other information or context taken into consideration. The goal is to assign a label to every pixel in the image, which provides a dense labeling of the image. The algorithm takes an image as input and generates a segmentation map where the pixel value (0,1,...255) of the image is transformed into class labels (0,1,...n). It is useful in applications where identifying the different classes of objects on the road is important. Semantic segmentation - the human and the dog are classified together as mammals and separated from the rest of the background. Panoptic Segmentation Panoptic segmentation is a combination of semantic and instance segmentation. It involves labeling each pixel with a class label and identifying each object instance in the image. This mode of image segmentation provides the maximum amount of high-quality granular information from machine learning algorithms. It is useful in applications where the computer vision model needs to detect and interact with different objects in its environment, like an autonomous robot. Panoptic segmentation Each type of segmentation has its unique characteristics and is useful in different applications. In the following section, let’s discuss the various applications of image segmentation. Image Segmentation Techniques Traditional Techniques Traditional image segmentation techniques have been used for decades in computer vision to extract meaningful information from images. These techniques are based on mathematical models and algorithms that identify regions of an image with common characteristics, such as color, texture, or brightness. Traditional image segmentation techniques are usually computationally efficient and relatively simple to implement. They are often used for applications that require fast and accurate segmentation of images, such as object detection, tracking, and recognition. In this section, we will explore some of the most common techniques. Thresholding Thresholding Thresholding is one of the simplest image segmentation methods. Here, the pixels are divided into classes based on their histogram intensity which is relative to a fixed value or threshold. This method is suitable for segmenting objects where the difference in pixel values between the two target classes is significant. In low-noise images, the threshold value can be kept constant, but with images with noise, dynamic thresholding performs better. In thresholding-based segmentation, the greyscale image is divided into two segments based on their relationship to the threshold value, producing binary images. Algorithms like contour detection and identification work on these binarized images. The two commonly used thresholding methods are: Global thresholding is a technique used in image segmentation to divide images into foreground and background regions based on pixel intensity values. A threshold value is chosen to separate the two regions, and pixels with intensity values above the threshold are assigned to the foreground region and those below the threshold to the background region. This method is simple and efficient but may not work well for images with varying illumination or contrast. In those cases, adaptive thresholding techniques may be more appropriate. Adaptive thresholding is a technique used in image segmentation to divide an image into foreground and background regions by adjusting the threshold value locally based on the image characteristics. The method involves selecting a threshold value for each smaller region or block, based on the statistics of the pixel values within that block. Adaptive thresholding is useful for images with non-uniform illumination or varying contrast and is commonly used in document scanning, image binarization, and image segmentation. The choice of adaptive thresholding technique depends on the specific application requirements and image characteristics. Image showing different thresholding techniques. Source: Author Region-based Segmentation Region-based segmentation is a technique used in image processing to divide an image into regions based on similarity criteria, such as color, texture, or intensity. The method involves grouping pixels into regions or clusters based on their similarity and then merging or splitting regions until the desired level of segmentation is achieved. The two commonly used region-based segmentation techniques are: Split and merge segmentation is a region-based segmentation technique that recursively divides an image into smaller regions until a stopping criterion is met and then merges similar regions to form larger regions. The method involves splitting the image into smaller blocks or regions and then merging adjacent regions that meet certain similarity criteria, such as similar color or texture. Split and merge segmentation is a simple and efficient technique for segmenting images, but it may not work well for complex images with overlapping or irregular regions. Graph-based segmentation is a technique used in image processing to divide an image into regions based on the edges or boundaries between regions. The method involves representing the image as a graph, where the nodes represent pixels, and the edges represent the similarity between pixels. The graph is then partitioned into regions by minimizing a cost function, such as the normalized cut or minimum spanning tree. Example of graph-based image segmentation. Source Edge-based Segmentation Edge-based segmentation is a technique used in image processing to identify and separate the edges of an image from the background. The method involves detecting the abrupt changes in intensity or color values of the pixels in the image and using them to mark the boundaries of the objects. The two most common edge-based segmentation techniques are: Canny edge detection is a popular method for edge detection that uses a multi-stage algorithm to detect edges in an image. The method involves smoothing the image using a Gaussian filter, computing the gradient magnitude and direction of the image, applying non-maximum suppression to thin the edges, and using hysteresis thresholding to remove weak edges. Example of canny edge detection Sobel edge detection is a method for edge detection that uses a gradient-based approach to detect edges in an image. The method involves computing the gradient magnitude and direction of the image using a Sobel operator, which is a convolution kernel that extracts horizontal and vertical edge information separately. Example of Sobel edge detection. Laplacian of Gaussian (LoG) edge detection is a method for edge detection that combines Gaussian smoothing with the Laplacian operator. The method involves applying a Gaussian filter to the image to remove noise and then applying the Laplacian operator to highlight the edges. LoG edge detection is a robust and accurate method for edge detection, but it is computationally expensive and may not work well for images with complex edges. Example of Laplacian of Gaussian edge detection. Clustering Clustering is one of the most popular techniques used for image segmentation, as it can group pixels with similar characteristics into clusters or segments. The main idea behind clustering-based segmentation is to group pixels into clusters based on their similarity, where each cluster represents a segment. This can be achieved using various clustering algorithms, such as K means clustering, mean shift clustering, hierarchical clustering, and fuzzy clustering. K-means clustering is a widely used clustering algorithm for image segmentation. In this approach, the pixels in an image are treated as data points, and the algorithm partitions these data points into K clusters based on their similarity. The similarity is measured using a distance metric, such as Euclidean distance or Mahalanobis distance. The algorithm starts by randomly selecting K initial centroids, and then iteratively assigns each pixel to the nearest centroid and updates the centroids based on the mean of the assigned pixels. This process continues until the centroids converge to a stable value. ‍ Showing the result of segmenting the image at k=2,4,10. Source Mean shift clustering is another popular clustering algorithm used for image segmentation. In this approach, each pixel is represented as a point in a high-dimensional space, and the algorithm shifts each point toward the direction of the local density maximum. This process is repeated until convergence, where each pixel is assigned to a cluster based on the nearest local density maximum. Source ‍Though these techniques are simple, they are fast and memory efficient. But these techniques are more suitable for simpler segmentation tasks as well. They often require tuning to customize the algorithm as per the use case and also provide limited accuracy on complex scenes. Deep Learning Techniques Neural networks also provide solutions for image segmentation by training neural networks to identify which features are important in an image, rather than relying on customized functions like in traditional algorithms. Neural nets that perform the task of segmentation typically use an encoder-decoder structure. The encoder extracts features of an image through narrower and deeper filters. If the encoder is pre-trained on a task like an image or face recognition, it then uses that knowledge to extract features for segmentation (transfer learning). The decoder then over a series of layers inflates the encoder’s output into a segmentation mask resembling the pixel resolution of the input image. The basic architecture of the neural network model for image segmentation. Source ‍ Many deep learning models are quite adept at performing the task of segmentation reliably. Let’s have a look at a few of them: U-Net U-Net is a modified, fully convolutional neural network. It was primarily proposed for medical purposes, i.e., to detect tumors in the lungs and brain. It has the same encoder and decoder. The encoder is used to extract features using a shortcut connection, unlike in fully convolutional networks, which extract features by upsampling. The shortcut connection in the U-Net is designed to tackle the problem of information loss. In the U-Net architecture, the encoders and decoders are designed in such a manner that the network captures finer information and retains more information by concatenating high-level features with low-level ones. This allows the network to yield more accurate results. U-Net Architecture. Source‍ SegNet SegNet is also a deep fully convolutional network that is designed especially for semantic pixel-wise segmentation. Like U-Net, SegNet’s architecture also consists of encoder and decoder blocks. The SegNet differs from other neural networks in the way it uses its decoder for upsampling the features. The decoder network uses the pooling indices computed in the max-pooling layer which in turn makes the encoder perform non-linear upsampling. This eliminates the need for learning to upsample. SegNet is primarily designed for scene-understanding applications. SegNet Architecture. Source DeepLab DeepLab is primarily a convolutional neural network (CNN) architecture. Unlike the other two networks, it uses features from every convolutional block and then concatenates them to their deconvolutional block. The neural network uses the features from the last convolutional block and upsamples it like the fully convolutional network (FCN). It uses the atrous convolution or dilated convolution method for upsampling. The advantage of atrous convolution is that the computation cost is reduced while capturing more information. The encoder-Decoder architecture of DeepLab v3. Source Foundation Model Techniques Foundation models have also been used for image segmentation, which divides an image into distinct regions or segments. Unlike language models, which are typically based on transformer architectures, foundation models for image segmentation often use convolutional neural networks (CNNs) designed to handle image data. Segment Anything Model Segment Anything Model (SAM) is considered the first foundation model for image segmentation. SAM is built on the largest segmentation dataset to date, with over 1 billion segmentation masks. It is trained to return a valid segmentation mask for any prompt, where a prompt can be foreground/background points, a rough box or mask, freeform text, or general information indicating what to segment in an image. Under the hood, an image encoder produces a one-time embedding for the image, while a lightweight encoder converts any prompt into an embedding vector in real time. These two information sources are combined in a lightweight decoder that predicts segmentation masks.‍ Source Metrics for Evaluating Image Segmentation Algorithms Pixel Accuracy Pixel accuracy is a common evaluation metric used in image segmentation to measure the overall accuracy of the segmentation algorithm. It is defined as the ratio of the number of correctly classified pixels to the total number of pixels in the image. Pixel accuracy is a straightforward and easy-to-understand metric that provides a quick assessment of the segmentation performance. However, it does not account for the spatial alignment between the ground truth and the predicted segmentation, which can be important in some applications. In addition, pixel accuracy can be sensitive to class imbalance, where one class has significantly more pixels than another. This can lead to a biased evaluation of the algorithm's performance. Dice Coefficient The dice coefficient measures the similarity between two sets of binary data, in this case, the ground truth segmentation and the predicted segmentation. The dice coefficient is calculated as Where intersection is the number of pixels that are correctly classified as positive by both the ground truth and predicted segmentations, and ground truth and predicted are the total number of positive pixels in the respective segmentations. The Dice coefficient ranges from 0 to 1, with higher values indicating better segmentation performance. A value of 1 indicates a perfect overlap between the ground truth and predicted segmentations. The Dice coefficient is a popular metric for image segmentation because it is sensitive to small changes in the segmentation and is not affected by class imbalance. However, it does not account for the spatial alignment between the ground truth and predicted segmentation, which can be important in some applications. Jaccard Index (IOU) The Jaccard index, also known as the intersection over union (IoU) score, measures the similarity between the ground truth segmentation and the predicted segmentation. It is formulated as Where intersection is the number of pixels that are correctly classified as positive by both the ground truth and predicted segmentations, and ground truth and predicted are the total number of positive pixels in the respective segmentations. The IoU score ranges from 0 to 1, with higher values indicating better segmentation performance. A value of 1 indicates a perfect overlap between the ground truth and predicted segmentations. The Jaccard index takes into account both the true positives and false positives and is not affected by class imbalance. It also accounts for the spatial alignment between the ground truth and predicted segmentations. Datasets for Evaluating Image Segmentation Algorithms The evaluation of image segmentation algorithms is a crucial task n computer vision research. To measure the performance of these algorithms, various benchmark datasets have been developed. We will be discussing three popular datasets for evaluating image segmentation algorithms. These datasets provide carefully annotated images with pixel-level annotations, allowing researchers to test and compare the effectiveness of their segmentation algorithms. Barkley Segmentation Dataset and Benchmark The Barkley Segmentation Dataset is a standard benchmark for contour detection. This dataset is intended for testing natural edge detection, which takes into account background boundaries in addition to object interior and exterior boundaries as well as object contours. It includes 500 natural images with carefully annotated boundaries collected from multiple users. The dataset is divided into three parts: 200 for training, 100 for validation, and the rest 200 for testing. Pascal VOC Segmentation Dataset The Pascal VOC Segmentation Dataset is a popular benchmark dataset for evaluating image segmentation algorithms. It contains images from 20 object categories and provides pixel-level annotations for each image. The dataset is divided into the train, validation, and test sets, with the test set used to evaluate the performance of segmentation algorithms. The Pascal VOC Segmentation Dataset has been used as a benchmark for various computer vision challenges, including the Pascal VOC Challenge and the COCO Challenge. MS COCO Segmentation Dataset The Microsoft Common Objects in Context (COCO) Segmentation Dataset is another widely used dataset for evaluating image segmentation algorithms. It contains over 330,000 images with object annotations, including segmentations of 80 object categories. The dataset is divided into the train, validation, and test sets, with the test set containing around 5,000 images. The MS COCO Segmentation Dataset is often used as a benchmark for evaluating segmentation algorithms in various computer vision challenges, including the COCO Challenge. Future Direction of Image Segmentation Auto-Segmentation with SAM Auto-segmentation refers to the process of automatically segmenting an image without human intervention. Auto-segmentation with Meta’s Segment Anything Model (SAM) has instantly become popular as it shows remarkable performance in image segmentation tasks. It is a single model that can easily perform both interactive segmentation and automatic segmentation. Since SAM is trained on a diverse, high-quality dataset, it can generalize to new types of objects and images beyond what is observed during training. This ability to generalize means that by and large, practitioners will no longer need to collect their segmentation data and fine-tune a model for their use case. Improvement in segmentation accuracy Improving segmentation accuracy is one of the main goals of researchers in the field of computer vision. Accurate segmentation is essential for various applications, including medical imaging, object recognition, and autonomous vehicles. While deep learning techniques have led to significant improvements in segmentation accuracy in recent years, there is still much room for improvement. Here are some ways researchers are working to improve segmentation accuracy: Incorporating additional data sources: One approach to improving segmentation accuracy is incorporating additional data sources beyond the raw image data. For example, depth information can provide valuable cues for object boundaries and segmentation, particularly in complex scenes with occlusions and clutter. Developing new segmentation algorithms: Researchers continuously develop new algorithms for image segmentation that can improve accuracy. For example, some recent approaches use adversarial training or reinforcement learning to refine segmentation results. Improving annotation quality: The quality of the ground truth annotations used to train segmentation algorithms is essential to achieving high accuracy. Researchers are working to improve annotation quality through various means, including incorporating expert knowledge and utilizing crowdsourcing platforms. Refining evaluation metrics: Evaluation metrics play a crucial role in measuring the accuracy of segmentation algorithms. Researchers are exploring new evaluation metrics beyond the traditional Dice coefficient and Jaccard index, such as the Boundary F1 score, which can better capture the quality of object boundaries. Integrate Deep Learning with Traditional Techniques While deep learning techniques have shown remarkable performance in segmentation tasks, traditional techniques such as clustering, thresholding, and morphological operations can still provide useful insights and improve accuracy. Here are some ways researchers are integrating deep learning with traditional techniques in image segmentation: Hybrid models: Researchers are developing hybrid models that combine deep learning with traditional techniques. For example, some approaches use clustering or thresholding to initialize deep learning models or post-process segmentation results. Multi-stage approaches: Multi-stage approaches involve using deep learning for initial segmentation and then refining the results using traditional techniques. For example, some approaches use morphological operations to smooth and refine segmentation results. Attention-based models: Attention-based models are a type of deep learning model that incorporates traditional techniques for computing attention weights within a feature map. Attention-based models can improve accuracy by focusing on relevant image features and ignoring irrelevant ones. Transfer learning: Transfer learning involves pretraining deep learning models on large datasets and then fine-tuning them for specific segmentation tasks. Traditional techniques such as clustering or thresholding can be used to identify relevant features for transfer learning. Applications of Image Segmentation Image segmentation has a wide range of applications in various fields, including medical imaging, robotics, autonomous vehicles, and surveillance. Here are some examples of how image segmentation is used in different fields: Medical imaging: Image segmentation is widely used in medical imaging for tasks such as tumor detection, organ segmentation, and disease diagnosis. Accurate segmentation is essential for treatment planning and monitoring disease progression. Robotics: Image segmentation is used in robotics for object recognition and manipulation. For example, robots can use segmentation to recognize and grasp specific objects, such as tools or parts, in industrial settings. Autonomous vehicles: Image segmentation is essential for the development of autonomous vehicles, allowing them to detect and classify objects in their environment, such as other vehicles, pedestrians, and obstacles. Accurate segmentation is crucial for safe and reliable autonomous navigation. Surveillance: Image segmentation is used in surveillance for detecting and tracking objects and people in real-time video streams. Segmentation can help to identify and classify objects of interest, such as suspicious behavior or potential threats. Agriculture: Image segmentation is used in agriculture for crop monitoring, disease detection, and yield prediction. Accurate segmentation can help farmers make informed decisions about crop management and optimize crop yields. Art and design: Image segmentation is used in art and design for tasks such as image manipulation, color correction, and style transfer. Segmentation can help to separate objects or regions of an image and apply different effects or modifications to them. Image Segmentation: Key Takeaways Image segmentation is a powerful technique that allows us to identify and separate different objects or regions within an image. It has a wide range of applications in fields such as medical imaging, robotics, and computer vision. In this guide, we covered various image segmentation techniques, including traditional techniques such as thresholding, region-based segmentation, edge-based segmentation, and clustering, as well as deep learning and foundation model techniques. We also discussed different evaluation metrics and datasets used to evaluate segmentation algorithms. As image segmentation continues to advance, future directions will focus on improving segmentation accuracy, integrating deep learning with traditional techniques, and exploring new applications in various fields. Auto-segmentation with the Segment Anything Model (SAM) is a promising direction that can reduce manual intervention and improve accuracy. Integration of deep learning with traditional techniques can also help to overcome the limitations of individual techniques and improve overall performance. With ongoing research and development, we can expect image segmentation to continue to make significant contributions to various fields and industries. Further Reading on Image Segmentation Comparing Two Object Segmentation Models: Mask-RCNN vs. Personalized-SAM Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges Visual Segmentation of “Simple” Objects for Robots Best practices in deep learning based segmentation of microscopy image Image segmentation: Papers with code

Nov 07 2022

15 M

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