Unsupervised segmentation of textured color images using Markov random field models

Abstract

An unsupervised segmentation algorithm which uses Markov random fields for modeling color texture is presented. These models characterize a texture in terms of spatial interaction within each color plane and interaction among different color planes. These models are used for segmentation in conjunction with an agglomerative clustering procedure that at each step minimizes a global performance functional based on the conditional pseudo-likelihood of the image. This algorithm is successfully applied to a range of textured color images of natural scenes. >