Multichannel filtering for image texture segmentation

A critical evaluation of the Gabor filters as opposed to filter banks used in image coding-in both full rate and critically sampled realizations concludes that, for a texture segmentation task, it is possible to use subsampled filters with only a modest degradation in segmentation accuracy-realizing considerable computational savings.

Abstract

Several approaches to multichannel filtering for texture classification and segmentation with Gabor filters have been proposed. The rationale presented for the use of the Gabor filters is their relation to models for the early vision of mammals as well as their joint optimum resolution in time and frequency. In this work we present a critical evaluation of the Gabor filters as opposed to filter banks used in image coding-in both full rate and critically sampled realizations. In the critically sampled case, tremendous computational savings can be realized. We further evaluate the commonly used octave band decomposition versus alternative decompositions. We conclude that, for a texture segmentation task, several filters provide approximately the same results as the Gabor filter and, most important, it is possible to use subsampled filters with only a modest degradation in segmentation accuracy-realizing considerable computational savings.