A Complete Dual-Cross Pattern for Unconstrained Texture Classification

Abstract

In order to perform unconstrained texture classification, this paper presents a novel and computationally efficient texture descriptor called Complete Dual-Cross Pattern (CDCP), which is robust to gray-scale changes and surface rotation. To extract CDCP, at first a gray scale normalization scheme is used to reduce the illumination effect and, then CDCP feature is computed from holistic and component levels. A local region of the texture image is represented by it's center pixel and difference of sign-magnitude transform (DSMT) at multiple levels. Using a global threshold, the gray value of center pixel is converted into a binary code named DCP center (DCP_C). DSMT decomposes into two complementary components: the sign and the magnitude. They are encoded respectively into DCP-sign (DCP_S) and DCP-magnitude (DCP_M), based on their corresponding threshold values. Finally, CDCP is formed by fusing DCP_S, DCP_M and DCP_C features through joint distribution. The invariance characteristics of CDCP are attained due to computation of pattern at multiple levels, which makes CDCP highly discriminative and achieves state-of-the-art performance for rotation invariant texture classification.