Texture and material classification with multi-scale ternary and septenary patterns

Abstract

This paper ideates, inspired by LBP and its variants, a novel local feature extraction operator for texture classification, referred to as Multi-scale Ternary and Septenary Pattern (MTSP). MTSP is a histogram-based feature reṕresentation that is composed of two single-scale STP and SSP (single-scale ternary and septenary patterns, respectively) encoders designed according to a novel set theory based pattern encoding scheme that integrates the concepts of both LQP’s and LTP’s operators. The essence of STP and SSP is to compute several virtual pixels based on various local and global image statistics and progressively encode interactions between local and non-local pixels by examining the directional information and differential excitation according to relationships between adjacent pixels rearranged in a variety of spatial arrangements. Unlike various parametric state-of-the-art texture operators that perform thresholding based on static thresholds, MTSP incorporates dynamic thresholds estimated automatically. MTSP descriptor has good ability as faithfully as possible to capture more detailed image information via complementary texture information generated from the fusion of both STP and SSP encoders. Experimental results show that MTSP ensures reliable performance stability over ten texture datasets and against several recent representative methods. In addition, the performance of MTSP is further proved statistically via the Wilcoxon signed rank test demonstrating thus that MTSP is a good candidate for texture modeling.