ESVC-based extraction and segmentation of texture features

Inspired by Krigeʹ variogram and the multi-channel filtering theory for human vision information processing, this paper proposes a novel algorithm for segmenting the textures based on experimental semi-variogram function (ESVF), which can simultaneously describe structural property and statistical property of textures. The single variogram function value (SVFV) and the variance distance obtained by ESVF are used as texture feature description for segmenting textures. The feasibility and effectiveness of the proposed method are demonstrated by testing on some texture images. The computational complexity of the proposed approach depends neither on the number of the textures nor on the number of the gray levels, and only on the size of the image blocks. e proved theoretically that the algorithm has the advantages of direction invariability and a higher sensitivity to different textures and can detect almost all kinds of the boundaries of the shape textures. Experimental results on the Brodatz texture databases show that the performance of this algorithm is superior to the traditional techniques such as texture spectrum, SIFT, k-mean method, and Gabor filters. The proposed approach is found to be robust, efficient, and satisfactory.