Optimal texture feature selection for the co-occurrence map

Abstract

Textures can be described by multidimensional co-occurrence histograms of several pixel gray levels and then classified, e.g., with nearest-neighbors rules. In this work, multidimensional histograms were reduced to two dimensions using the Tree-Structured Self-Organizing Map, here called the Co-occurrence Map. The best components of the co-occurrence vectors, i.e., the spatial displacements minimizing the classification error were selected by exhaustive search. The fast search in the tree-structured maps made it possible to train about 14 000 maps during the feature selection. The highest classification accuracies were obtained using variance-equalized principal components of the co-occurrence vectors. Texture classification with our reduced multidimensional histograms was compared with classification using either channel histograms or standard co-occurrence matrices, which were also selected to minimize the classification error. In all comparisons, the multidimensional histograms performed better than the two other methods.