Feature fusion with neighborhood-oscillating tabu search for oriented texture classification

Abstract

This paper develops two techniques of oriented texture analysis: the modified Gabor filters (MGF) and the Gaussian Markov random field model with circular neighborhoods (CGMRF). The neighborhood-oscillating tabu search algorithm (NOTS) is proposed to solve the MGF/CGMRP feature fusion problem, and compared with classical algorithms, such as sequential forward selection and sequential forward floating selection methods. Based on the experimental results, NOTS is shown to be a promising tool for feature fusion, and the MGF/CGMRF fused features achieved by NOTS perform better than either MGF or CGMRP alone according to the Fisher criterion and classification accuracy. © 2005 by International Federation for Information Processing.