Co-occurrence map: quantizing multidimensional texture histograms
Pattern Recognition Letters
Reduced Multidimensional Co-Occurrence Histograms in Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We have recently 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 was developed further by selecting the co-occurring color components and the number of code vectors to minimize the classification error. A genetic algorithm was used for the optimization, and the iterative searches for the best parameters were enabled 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 provided significantly higher classification accuracies that two- or three-dimensional histograms of intra- and interpixel co-occurrences. They also performed better than a Markov random field model.