Co-occurrence based features for automatic texture classification using neural networks

Anwar Muhamad; Farzin Deravi

doi:10.1117/12.130855

16 December 1992 Co-occurrence based features for automatic texture classification using neural networks

Anwar Muhamad, Farzin Deravi

Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992) https://doi.org/10.1117/12.130855
Event: San Diego '92, 1992, San Diego, CA, United States

Abstract

In this paper some of the commonly used features for texture classification based on co- occurrence statistics are studied. First, the classification capabilities of individual features in classifying among a small and a large number of texture images are evaluated. Then, the capabilities of different combinations of texture features are examined in order to establish a reduced set of features for maximum performance. An artificial neural network is used to test the suitability of promising feature groups for texture classification. It is shown that the features considered may be broadly divided into two groups in terms of their classification performance. It is also shown that with a judicious choice of features and a well trained neural network classifier, high recognition rates can be achieved.

Citation Download Citation

Anwar Muhamad and Farzin Deravi "Co-occurrence based features for automatic texture classification using neural networks", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130855

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