Reduced multidimensional histograms in color texture description

We (1998) have developed methods for the description of monochrome and color textures with models of multidimensional co-occurrence distributions. The models are histograms of quantized multidimensional co-occurrence vectors obtained using the code words of vector quantizer as indexes of histogram bins. In the present study, the color texture analysis is further developed by selecting the co-occurring color components and the number of code vectors to minimize the classification error. The genetic algorithm is used for the optimization, and the iterative searches for the best parameters are performed by a vector quantizer with a short training time: the two-stage vector quantizer. The reduced multidimensional color histograms of 2-by-2-pixel values provide significantly higher classification accuracies than two- or three-dimensional histograms of intra- and interpixel co-occurrences. They also performed better than a Markov random field model