The subspace method of pattern recognition has been developed for fast and accurate classification of high-dimensional feature vectors, especially power spectra and distribution densities. The basic algorithms for class subspace construction are statistically motivated, and the classification is based on inner products. In texture analysis, this method has been previously applied for two-dimensional spatial frequency spectra. In this work we show that a feasible method for texture window classification is to use a smoothed cooccurrence matrix as the feature vector and to define texture classes for such representations by the subspace method. These texture subspaces seem to capture the characteristic second-order properties of texture fields. Results are given using various natural and synthetic textures.
KeywordsEntropy Beach Autocorrelation
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