10 a] What is image segmentation? Classify the image segmentation algorithms.
Image segmentation is the process of partitioning an image into multiple segments or regions, where each segment consists of pixels that share similar attributes such as color, intensity, or texture. The main objective is to simplify the image’s representation, making it easier to analyze and process. Segmentation is often used to identify objects, boundaries, and other relevant structures within an image. It is a crucial step in many computer vision tasks, including object detection, recognition, and image editing. Techniques for segmentation range from simple thresholding to more complex methods like clustering, edge detection, and deep learning-based approaches.
Classification of Image Segmentation
Image segmentation methods can be classified into three categories based on the level of user interaction:
- Manual Segmentation: In this approach, an expert manually traces the boundaries of the region of interest (ROI) using specialized software. The expert makes all segmentation decisions, and the software may assist by connecting open contours into closed regions, which are then converted into control points and connected by splines. While accurate, manual segmentation is time-consuming, subjective, and prone to variations among different observers.
- Automatic Segmentation: These algorithms perform segmentation without any human intervention, making them ideal for processing large numbers of images. Automatic methods analyze the image and segment structures based on predefined criteria, offering consistent results with minimal user input.
- Semi-Automatic Segmentation: This approach combines both manual and automatic methods. Human intervention is required at the initial stage, typically to provide seed points indicating the ROI. The algorithm then automatically completes the segmentation process. Region-growing techniques are a common example, where the program processes the segmentation after the initial seeds are placed by the user.
Segmentation algorithms can also be classified based on pixel similarity relationships:
- Contextual (Region-Based or Global) Algorithms: These methods group pixels based on common properties, such as color or texture, by exploiting the relationships among neighboring pixels. They are also known as global algorithms because they consider the entire image context.
- Non-Contextual (Pixel-Based or Local) Algorithms: These algorithms focus on individual pixel properties, ignoring their relationships with neighboring pixels. They aim to identify discontinuities in the image, such as edges or isolated lines, and group them into regions accordingly.