Edge-suppressed color clustering for image thresholding

This paper discusses the development of an iterative algorithm for fully automatic (gross or fine) segmentation of color images. The basic idea here is to automate segmentation for on-line operations. This is needed for such critical applications as internet communication, video indexing, target tracking, visual guidance, remote control, and motion detection. The method is composed of an edge-suppressed clustering (learning) and principal component thresholding (classification) step. In the learning phase, image clusters are well formed in the (R,G,B) space by considering only the non-edge points. The unknown number (N) of mutually exclusive image segments is learned in an unsupervised operation mode developed based on the cluster fidelity measure and K-means algorithm. The classification phase is a correlation-based segmentation strategy that operates in the K-L transform domain using the Otsu thresholding principal. It is demonstrated experimentally that the method is effective and efficient for color images of natural scenes with irregular textures and objects of varying sizes and dimension.