Texture Classification Using Complete Texton Matrix

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Y.Sowjanya Kumari 1,* V.Vijaya Kumar 2 V. Vijayalakshmi 3

1. Dept. CSE, SACET, Chirala, Andhrapradesh, India

2. Dean Dept. of CSE & IT and Director CACR, Anurag Group of Institutions, Hyderabad, India

3. Dept. CSE, Jawaharlal Nehru Technological University, Kakinada, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2017.10.07

Received: 9 Jun. 2017 / Revised: 29 Jun. 2017 / Accepted: 13 Jul. 2017 / Published: 8 Oct. 2017

Index Terms

Texton, Histogram, quantization, color gradient, dimensionality


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 co-occurrence 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 non-overlapped 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.

Cite This Paper

Y.Sowjanya Kumari, V. Vijaya Kumar, Ch. Satyanarayana," Texture Classification Using Complete Texton Matrix", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.10, pp. 60-68, 2017. DOI: 10.5815/ijigsp.2017.10.07


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