Texture Classification using Texton Co-Occurrence Matrix Derived from Texture Orientation
Sujatha.B1, Chandra Sekhar Reddy2, P Kiran Kumar Reddy3
1Sujatha.B, His Department Name, University/ College/ Organization Name, City Name, Country Name,.
2Chandra Sekhar Reddy, His Department Name, University/ College/ Organization Name, City Name, Country Name.
3P Kiran Kumar Reddy, His Department Name, University/ College/ Organization Name, City Name, Country Name.
Manuscript received on January 01, 2013. | Revised Manuscript received on January 02, 2013. | Manuscript published on January 05, 2013. | PP: 18-23 | Volume-2, Issue-6, January 2013. | Retrieval Number: F1071112612/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The present paper derived a new co-occurrence matrix based on textons and texture orientation for rotation invariant texture classification of 2D images. The new co-occurrence matrix is called as Texton and Texture Orientation Co-occurrence Matrix (T&TO-CM). The Co-occurrence Matrix (CM) characterizes the relationship between the values of neighboring pixels, while the histogram based techniques have high indexing performance. If the CM is used to represent image features directly, then the dimension will be high and the performance is decreased. On the other hand, if histogram is used to represent image features, the spatial information will be lost. Texture Classification based on T&TO-CM, integrates color, texture and edge features of an image. The proposed T&TO-CM is used to describe the spatial correlation of textons and texture orientation for texture classification. T&TO-CM can capture the spatial distribution of edges, and it is an efficient texture descriptor for images with heavy textural presence. The proposed method is computationally attractive as it computes different features with limited number of selected pixels. The experimental results indicate the efficacy of the present method over the various other methods.
Keywords: Co-occurrence Matrix;Texton, Texture Orientation