Supervised Pixel-Based Texture Classification with Gabor Wavelet Filters

Abstract

This paper proposes an efficient technique for pixel-based texture classification based on multichannel Gabor wavelet filters. The proposed technique is general enough to be applicable to other texture feature extraction methods that also characterize the texture around image pixels through feature vectors. During the training stage, a clustering technique is applied in order to compute a suitable set of prototypes that model every given texture pattern. Multisize evaluation windows are also utilized for improving the accuracy of the classifier near boundaries between regions of different texture. Experimental results with Brodatz compositions show the benefits of the proposed scheme in contrast with alternative approaches in terms of efficiency, memory and classification rates.