Texture Classification Using Adaptive Feature Extraction Technique

Inspired by wavelet modulus maxima and six basic textural properties, i.e. coarseness, contrast, directionality, line-likeness, regularity, and roughness, this paper proposes the use of the ratio of wavelet extrema numbers taken from the horizontal and vertical counts respectively as a texture feature which is called aspect ratio of extrema number (AREN). Moreover, a novel approach using genetic algorithms (GAs) for texture segmentation, called iterative feature extraction (IFE), is proposed to iteratively search and select for an over complete wavelet feature vector based on AREN feature with a desired window that provides optimal classification accuracy. We demonstrate the efficiency of GHM multi wavelet frames in texture discrimination with respect to D4 scalar wavelet frames.