A complete dual-cross pattern for unconstrained texture classification
Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
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.
Roy, Swalpa K.; Chanda, Bhabatosh; Chaudhari, Bidyut B.; Ghosh, Dipak K.; and Dubey, Shiv Ram, "A complete dual-cross pattern for unconstrained texture classification" (2018). Conference Articles. 20.