TEXTURE CLASSIFICATION BASED ON OVERLAPPED TEXTON CO-OCCURRENCE MATRIX (OTCOM) FEATURES

Abstract

Abstract: The pattern identification problems such as stone, rock categorization and wood recognition are used texture classification technique due to its valuable usage in it. Generally, texture analysis can be done one of the two ways i.e. statistical and structural approaches. More problems are occurred when working with statistical approaches in texture analysis for texture categorization. One of the most popular statistical approaches is Gray Level Co-occurrence Matrices (GLCM) approach. This approach is used to discriminating different textures in images. This approach gives better accuracy results but this takes high computational cost. Usually, texture analysis method depends upon how the texture features are extracted from the image to characterize image. Whenever a new texture feature is derived it is tested whether it is precisely classifies the textures or not. Texture features are most important for precise and accurate texture classification and also important that the way in which they are extracted and applied. The present paper derived a new co-occurrence matrix based on overlapped textons patterns. The present paper generates overlapped texton patterns and generates co-occurrence matrices derived a new matrix called Overlapped Texton Co-occurrence Matrices (OTCoM) for stone texture classification. The present paper integrates the advantages of co-occurrence matrix and texton image by representing the attribute of co-occurrence. The co-occurrence features extracted from the OTCoM provides complete texture information about a texture image. The proposed method is experimented on Vistex, Brodatz textures, CUReT, Mayang, Paul Brooke, and Google color texture images. The experimental results indicate the proposed method classification performance is superior to that of many existing methods.