  Texture analysis and classification remain as one of the biggest challenges
for the field of computer vision and pattern recognition. On this matter, Gabor
wavelets has proven to be a useful technique to characterize distinctive
texture patterns. However, most of the approaches used to extract descriptors
of the Gabor magnitude space usually fail in representing adequately the
richness of detail present into a unique feature vector. In this paper, we
propose a new method to enhance the Gabor wavelets process extracting a fractal
signature of the magnitude spaces. Each signature is reduced using a canonical
analysis function and concatenated to form the final feature vector.
Experiments were conducted on several texture image databases to prove the
power and effectiveness of the proposed method. Results obtained shown that
this method outperforms other early proposed method, creating a more reliable
technique for texture feature extraction.
