Texture Classification with Single- and Multiresolution Co-Occurrence Maps

Abstract

We have developed methods for the classification of textures with multidimensional co-occurrence histograms. Gray levels of several pixels with a given spatial arrangement are first compressed linearly and the resulting multidimensional vectors are quantized using the self-organizing map. Histograms of quantized vectors are classified by matching them with precomputed texture model histograms. In the present study, a multiple resolution technique in linear compression of pixel values is evaluated. The multiple resolution linear compression was made with a local wavelet transform. The vectors were quantized with the tree-structured variant of the self-organizing map. In the tree-structured self-organizing map, the quantization error is reduced, in comparison to the traditional tree-structured codebook, by limited lateral searches in topologically-ordered neighborhoods. The performance of multiresolution texture histograms was compared with single-resolution histograms. The histogram method was compared with three well-established methods: co-occurrence matrices, Gaussian Markov random fields, and multiresolution Gabor energies. The results for a set of natural textures showed that the performance of single- and multiresolution texture histograms was similar. Thus, the benefit of multiresolution analysis was overridden by the multidimensionality of our texture models. Our method gave significantly higher classification accuracies than the three other methods.