Color Image Segmentation Using Hierarchical Merge Tree

Image Segmentation Using Hierarchical Merge Tree is analyzed in this paper by means of fast fuzzy c-means clustering algorithm and fuzzy membership function. Proposed work is done for the color segmentation, as previous research presents a connected coherence tree algorithm (CCTA) for image segmentation with no prior knowledge. It aims to find regions of semantic coherence based on the proposed epsilon-neighbor coherence segmentation criterion. RGB image is converted to Lab color space by taking average of each color component individually, then the average value of the color of small region selected is compared with all the pixels of the image and finally the desired object gets segmented efficiently. This algorithm runs in small time with much accuracy since, FFCM avoids some unnecessary comparisons between the pixels.