A comparative study of matrix measures for maximum likelihood texture classification

The performance of various matrix features in classifying synthetic and natural textures is compared by using the features directly in a maximum likelihood texture classifier. The matrix texture features examined are the spatial gray-level dependence matrix (SGLDM), the neighboring gray-level dependence matrix (NGLDM) and the neighboring spatial gray-level dependence matrix (NSGLDM). It is shown that, in general, for natural textures that are stochastic in nature, any of the texture features based on the NGLDM for distance (d) equal to 1 would be good choices as measures for texture classification. The parameter alpha should be chosen to maximize classification performance. For d=1, these measures require much fewer computations than the SGLDM with comparable performance. Also, the NGLDM-based measure require fewer computations for d=1 than for larger distances. For highly structured textures that may be characterized by large primitive elements, the NSGLDM can be used to extract texture information at larger distances while maintaining its relative computational efficiency.<
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