Graph Cut Image Segmentation Technique

In image processing, graph cut segmentation is an algorithm designed to separate an image into two classes using color characteristics. This technique classifies pixels within the image to isolate different regions and objects. The fundamental principle involves representing the image as an undirected graph where pixels serve as nodes, with edges connecting adjacent pixels. By finding the minimum cut in this graph, the algorithm effectively partitions the image into distinct regions. Implementation typically involves creating adjacency matrices where edge weights represent color similarity and spatial proximity between pixels. Key functions often include calculating color histograms, constructing energy functions that combine data terms (color similarity) and smoothness terms (spatial continuity), and applying max-flow/min-cut algorithms for optimization. This method has been widely adopted in medical image analysis, computer vision applications, and object recognition systems due to its robustness in handling complex boundaries and texture variations.