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Image Segmentation: 10 Concepts, 5 Use Cases and a Hands-on Guide [Updated 2024]

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AI systems today can easily identify bread in an image of a dinner spread we hand to it.

But what if we ask it which dish has the most fat? The task gets trickier. To answer this, the AI needs to recognize each food item and understand their composition at a granular level. This shift from basic recognition to nuanced understanding was the subject of an intriguing study published in August 2023.

A Reasoning Segmentation Task

The paper introduced "LISA" (Language Instructed Segmentation Assistant), a model that performs "Reasoning Segmentation" tasks. To answer complex questions, LISA first segments the image, then pairs this with data from a database, mimicking human reasoning.

Though "Reasoning Segmentation" is a recent development, image segmentation has a long history of propelling computer vision. Whether it's allowing self-driving cars to accurately read road markings or aiding medical systems to identify trouble spots in scans, the power of segmentation is clear.

In this post, we’ll explore the world of image segmentation. Whether you're an engineer or developer, we'll cover key concepts and real-world applications to showcase segmentation's invaluable role in AI. Let's dive in!

What is Image Segmentation?

Image segmentation is a crucial task in computer vision that splits a digital image into distinct segments. This process labels each pixel in the image, identifying objects or other significant elements. This simplifies the image and allows further analysis for each segment.

Image Segmentation Example - Road Surveillance Footage

Think of an image with a dog and a cat. Image segmentation helps us locate each animal within the image, not just recognize them. This process occurs at a pixel level, offering a detailed comprehension of the image layout. It helps determine to which object pixels belonging and how model outputs represent the image.

Why Image Segmentation is Important?

Image segmentation is an essential preprocessing step enabling many computer vision capabilities. It plays a key role in object detection by focusing algorithms on specific image regions, improving accuracy and speed. Segmentation is extensively used in medical imaging to isolate anatomical structures and regions of interest. It aids autonomous vehicles in navigating environments by distinguishing roads, pedestrians, and other cars. It also enables face recognition, satellite analysis, video surveillance, and more. By providing granular image details, segmentation supplies comprehensive and detailed information about images, making it an invaluable tool in the field of computer vision. This detailed, pixel-level information is what you train the model on, leading to more accurate and reliable results.

10 Key Concepts You Need to Know About Image Segmentation

Navigating a new field can be intimidating, especially when it's full of complex jargon and technical terms. However, understanding key concepts can significantly close the knowledge gap between novices and experts. Let's explore 10 of these crucial terms to understand the world of image segmentation better.

1. Ground Truth

Ground truth is the accurate segmentation of an image used to evaluate the performance of segmentation algorithms. It involves manual annotation of images by humans to identify object boundaries or areas of interest. In image segmentation, it's the precise labeling of every pixel in the image. It serves as a reference for training and validating your machine learning models.

2. Object and Region

Image segmentation can identify distinct objects and regions within an image. An object refers to the entity or feature of interest in an image, while a region is a group of connected pixels that share common properties (color, intensity, texture). The aim of segmentation is to identify these objects and regions for further analysis.

3. Region of Interest (ROI)

The Region of Interest (ROI) in image segmentation is a specific part of an image selected for further analysis. This region typically contains the objects or features of interest. By focusing only on the ROI, the efficiency of the image processing task can be significantly increased. It also helps reduce noise and irrelevant information outside the ROI, leading to more accurate results.

4. Masking and Image Masks

Masking is the process of pinpointing pixel locations associated with each object or area. This action generates binary images, where the pixels of the objects are seen as the foreground and the remaining as the background. In essence, the mask is a binary image with the same dimensions as the input. Here, pixels corresponding to the object of interest are True (or 1), while the rest are False (or 0).

5. Semantic Segmentation

Image segmentation can be broadly divided into three tasks: semantic segmentation, instance segmentation, and panoptic segmentation. These types of image segmentation offer different levels of detail and are chosen based on the specific application's requirements. Then, what is semantic image segmentation? It is an approach where every pixel in an image is assigned to a specific class or category. However, while it identifies the classes of objects present, it does not distinguish between different instances of the same class.

6. Instance Segmentation

Instance Segmentation classifies pixels based on the instances of an object. It detects and separates each object of interest in the image. Unlike semantic segmentation, it does not predict the class or category of each instance. This method is particularly useful in situations where individual objects need to be identified and tracked.

7. Panoptic Segmentation

Panoptic Segmentation combines the strengths of both semantic and instance segmentation. It identifies the class of each pixel (like semantic segmentation) and distinguishes different instances of the same class (like instance segmentation). This approach is especially useful in applications that require a high level of detail and understanding of the scene, such as autonomous driving systems.

8. Region-based Image Segmentation

Region-based segmentation is a key technique in image processing, used for dividing an image into multiple regions that share common characteristics, such as color, texture, or intensity. This process identifies and groups similar pixels into regions or clusters, and then manipulates these regions (through merging or splitting) until a desired level of segmentation is achieved.

9. Polygon Annotation vs. Segmentation

Polygon annotation and segmentation are two different techniques used in image annotation work. Polygon annotation involves drawing bounding polygons around objects of interest in an image, capturing its basic shape and location. Segmentation, on the other hand, involves precisely outlining the edges and contours of an object at the pixel level. Segmentation thus provides more accurate object delineation critical for tasks like object detection and semantic segmentation.

10. Segment Anything Model (SAM)

The Segment Anything Model (SAM) was released in June 2022 as an open-source project by Meta AI. SAM employs a unified framework for semantic and instance image segmentation. Pre-trained on diverse data, it can segment over 5,000 objects without re-training. However, SAM has limitations. It may not meet the needs for precision in medical, autonomous vehicle, and security applications where errors are unacceptable. The model also faces issues like computing costs and data security. While promising, SAM does not make manual annotation obsolete yet. Human expertise is still required for niche, professional use cases demanding precision.

Real-World Applications: 5 Image Segmentation Use Cases

Now that we have covered the core concepts, let's look at five real-world use cases of image segmentation, ranging from autonomous vehicles and medical imaging analysis to satellite image analysis, smart agriculture, and industrial inspection.

Autonomous Vehicles

Image Segmentation for Autonomous Vehicles AI Models

Image and video segmentation is critical for training robust computer vision models to enable autonomous driving capabilities. Segmentation provides precise delineation of objects like vehicles, pedestrians, roads, and traffic signs. This pixel-level understanding feeds accurate perception algorithms for tasks like obstacle avoidance, lane keeping, and navigation. Segmented data also trains models to segment and interpret new driving scenes. Reliable autonomy requires extensive, diverse driving imagery with meticulous semantic, instance, and panoptic segmentation masks labeling all environmental elements. This high-quality training data produces self-driving models that precisely perceive complex, dynamic driving environments.

Medical Imaging Analysis

Image Segmentation for Medical Imaging Analysis AI Models

AI models trained on segmented medical imaging datasets are revolutionizing healthcare diagnostics and treatment planning. Segmentation allows isolating and measuring specific anatomy in modalities like CT, MRI, and ultrasound. For example, organ, tumor, and tissue segmentation quantify volumes and morphology. Segmentation also aids visualization, surgery planning, and pathology detection by highlighting anatomical structures. Robust AI models for medical image segmentation require training data with diverse scanned images expertly annotated at the pixel-level. This powers precision segmentation of novel scans to augment workflows. Overall, medical segmentation enables quantitative, automated analysis of anatomical imagery impossible through human inspection alone.

Analysis of Satellite Images

Image Segmentation for Satellite Images Analysis AI Models

For satellite image analysis, AI models trained with segmentation datasets can identify various geographic and man-made features with high precision. They help in tasks like land cover classification, urban planning, environmental monitoring, and disaster management. For instance, segmentation models can differentiate forests, water bodies, urban areas, and agricultural lands in a satellite image, providing valuable data for land use planning and climate change studies. Image segmentation algorithms can also identify damaged structures or flooded areas in post-disaster satellite images, aiding in efficient disaster response.

Smart Agriculture

Image Segmentation for Smart Agriculture AI Models

In agriculture, segmentation of aerial imagery provides intelligence on crop health and land use. Segmenting fields, soil, irrigation, and crops in satellite or drone photographs enables advanced farm analytics. Computer vision models can automate plant counting, assess ripeness and disease, monitor growth, and estimate yields by leveraging precise segmentation. Image segmentation dataset allows developing highly accurate models even for niche crops. Segmentation thereby unlocks automation to optimize farming operations, catch issues early, and improve sustainability.

Industrial Inspection

Image Segmentation for Industrial Inspection AI Models

AI models trained with segmented image datasets are transforming industrial inspection processes by automating defect detection and quality control. They can identify and localize defects in products or components based on images or video feeds, reducing reliance on manual inspection. For instance, in electronics manufacturing, segmentation models can detect faulty components on a circuit board. In the automotive industry, they can identify dents or scratches on vehicle bodies. These applications not only improve inspection accuracy and speed but also reduce costs and enhance product quality.

Hands-on: A Step-by-Step Guide to Image Segmentation on BasicAI Cloud

You're likely eager to start your own image segmentation projects after learning about real-world applications. The following guide will lead you through each stage to segment images using BasicAI Cloud. This paves the way for robust, accurate AI models. Let's get started!

Step 1. Getting Your Project Started: Data Preparation

Your journey to high-quality image segmentation begins with preparing your data. Acceptable image formats include JPG, JPEG, PNG, BMP, TIFF, and WEBP. You can upload individual images or zip folders containing multiple images.


Image Segmentation on BasicAI Cloud - Build Dataset


Image Segmentation on BasicAI Cloud - Upload Image Data

To get started, navigate to BasicAI Cloud and select the 'Datasets' tab. Click on "+Create" to establish a new dataset. Enter a name for your dataset and select the appropriate 'Dataset Type'. In this case, let's select "Image" as the type and name our dataset "Image Segmentation". To upload your images for segmentation, click the "Upload" button located in the top right corner and simply drag and drop your local data into the dataset.

Step 2. Building Your Ontology

The next step is creating an Ontology, which is an advanced, customizable labeling system introduced by BasicAI to BasicAI Cloud. For our example task, we'll be performing panoptic segmentation on a road surveillance image, focusing on three classes: Pedestrians, Zebra Crossing, and Road.


Image Segmentation on BasicAI Cloud - Ontology Building


Image Segmentation on BasicAI Cloud - Ontology Setting

To build your Ontology, go to the "Ontology" tab on the Dataset page and click "Create". You'll need to create three classes, each with "Mask" as the "Tool Type". Assign each class a unique name and color to differentiate them.

Step 3. Expertly Segment Your Image

With your data and Ontology ready, it's time to start annotating! Return to the "Data" tab and click "Annotate" on your image data. To switch to segmentation mode, click "Segmentation" on the right of the annotation interface.

There are three manual segmentation tools available: Brush, Polygon, and Fill.

​The Brush Tool allows you to free-draw lines, outlines, or masks and is ideal for annotating irregular shapes or doodles. It offers high precision, especially over large areas, although at a slower speed. For small areas, it maintains high precision but with a quicker adjustment speed.

​The Polygon Tool, on the other hand, is designed for creating regular polygon shapes. It's a quick tool for annotating large areas, although it may be less precise for highly irregular targets. For small areas, it can be more error-prone and challenging to adjust. This tool is perfect for annotating regular boundaries and object contours.

Use these tools to segment your image with the labels defined in your Ontology. For an in-depth guide to using these tools, watch the following video:

Step 4. Data Export: Ta king Your Results to the Next Level

Once all regions of interest (ROIs) have been annotated, save your work by clicking "Save" on the top right of the annotation interface. After closing the annotation interface, select the segmented image and click "Export". Create an export task, choose your preferred format, run the task, and finally, download your results. You're now ready to use your segmented data for your AI model training!

Image Segmentation on BasicAI Cloud - Data Export

Bonus: Automatic Image Segmentation with Imbedded SAM

While manual segmentation provides high accuracy, it can be time-consuming. BasicAI Cloud, the AI-powered training data platform, has integrated the Segment Anything Model (SAM) to automate the initial segmentation process. This feature, coupled with AI-assisted tools, significantly accelerates your image segmentation workflow. To enhance your project with this powerful feature, feel free to contact us.

Ready to Kick Off Your Image Segmentation Project? Go to the FREE BasicAI Cloud and Start Your Training Data Journey