Compressing higher-order co-occurrences for texture analysis using the self-organizing map

Texture analysis is useful in many computer vision applications. One of the most useful texture feature sets is based on second-order co-occurrences of gray levels of pixel pairs. An extension of the co-occurrences to higher orders is prevented by the large size of the multidimensional arrays. We quantize the higher-order co-occurrences by the self-organizing map, called the co-occurrence map, which allows a flexible two-dimensional representation of co-occurrence histograms of any order. Experiments with natural gray level and color textures show that the method is effective in texture classification and segmentation