Reduced Multidimensional Co-Occurrence Histograms in Texture Classification

Textures are frequently described using co-occurrence histograms of gray levels at two pixels in a given relative position. Analysis of several co-occurring pixel values may benefit texture description but is impeded by the exponential growth of histogram size. To make use of multidimensional histograms, we have developed methods for their reduction. The method described here uses linear compression, dimension optimization, and vector quantization. Experiments with natural textures showed that multidimensional histograms reduced with the new method provided higher classification accuracies than the channel histograms and the wavelet packet signatures. The new method was significantly faster than our previous one.

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