Reducing the Computational Complexity of Markov Random Fields within an Arbitrarily Large Texture Label Space

Abstract

This work proposes a novel idea, called SOIL, for reducing the computational complexity of the maximum a posteriori optimization problem using Markov random field by exploiting the local characteristics so that the searching in a virtually infinite label space is confined in a small finite space. Globally the number of labels allowed is as many as the number of image sites while locally the labels assigned to the 4-neighbour plus a random one. Neither the prior knowledge about the number of classes nor the estimation phase of the class number is required in this work. The proposed method is applied to the problem of texture segmentation and the result is compared with those obtained from conventional methods.