Texture Classification Using Complete Texton Matrix
Y. Sowjanya Kumari, V. Vijaya Kumar, Ch. Satyanarayana · International Journal of Image Graphics and Signal Processing · 2017
This paper presents a complete image feature representation, based on texton theory proposed by Julesz's, called as a complete texton matrix (CTM)for texture image classification.The present descriptor can be viewed as an improved version of texton cooccurrence matrix (TCM) [1] and Multi-texton histogram (MTH) [2].It is specially designed for natural image analysis and can achieve higher classification rate.TheCTM can express the spatial correlation of textons and can be considered as a generalized visual attribute descriptor.This paper initially quantized the original textures into 256 colors and computed color gradient from RGB vector space.Then the statistical information of eleven derived textons, on a 2 x 2 grid in a nonoverlapped manner are computed to describe image features more precisely.To reduce the dimensionality the present paper extended the concept of present descriptor and derived a compact CTM (CCTM).The proposed CTM and CCTM methods are extensively tested on the Brodtaz, Outex and UIUC natural images.The results demonstrate the superiority of the present descriptor over the state-of-art representative schemes such as uniform LBP (ULBP), local ternary pattern (LTP), complete -LBP (CLBP), TCM and MTH.