Image Classification Based on Centre Symmetric Fuzzy Texture Unit Matrix

Texture is an important spatial feature, useful for identifying objects or regions of interest in an image. Statistical approaches have extensively studied in the texture analysis and classification. The most popular statistical methods used to measure the textural information of images are the Grey Level (GL) Co-occurrence Matrix (CM) and the Texture Spectrum (TS) Approach. The present paper combined the features of Centre Symmetric Fuzzy Texture Unit Matrix (CSFTUM) and GLCM and derived a new matrix called CSFTU-CM for texture classification. The proposed CSFTU-CM reduces the size of the TU matrix from 6561 to 67 in the case of original texture spectrum and 2020 to 67 in the case of Fuzzy Texture Spectrum (FTS) approach. Thus, it reduces the overall complexity. The co- occurrence features extracted from the CSFTU-CM provides complete texture information about an image. The experimental results indicate the proposed method classification performance is superior to that of many methods