Image Segmentation Approaches

Five Common Image Segmentation Techniques

Image segmentation involves partitioning an image into multiple segments to simplify its representation and make it more meaningful and easier to analyze.

Two primary approaches dominate the field of image segmentation:

Similarity Approach
Discontinuity Approach

Each approach has its methods and applications, tailored to different types of images and objectives.

Similarity Approach

The similarity approach in image segmentation groups pixels or regions based on their similar properties. This method assumes that regions with similar characteristics should be grouped together.

Common techniques in the similarity approach include:

Thresholding
Region Growing
Clustering (e.g., K-means Clustering, Mean Shift Clustering)
Graph-Based Segmentation (e.g., Normalized Cuts, Min-Cut/Max-Flow)

Discontinuity Approach

The discontinuity approach focuses on detecting and exploiting abrupt changes in intensity or color to identify boundaries between different regions. This approach is useful for images where regions are defined by clear edges.

Common techniques in the discontinuity approach include:

Edge Detection (e.g., Sobel Operator, Canny Edge Detector)
Line Detection (e.g., Hough Transform)
Corner Detection (e.g., Harris Corner Detector)

These approaches and techniques provide the foundation for effectively segmenting images, making them crucial for various applications in computer vision and image processing.