Gabor wavelets combined with volumetric fractal dimension applied to texture analysis

• A method for enhancing the Gabor Wavelets is proposed.
• The Gabor space is rich of information and tipcally not very well explored.
• This work proposes a combination of Gabor and fractal dimension descriptors.
• Volumetric fractal dimension descriptors (VFDD) are extracted from the Gabor space.
• Gabor + VFDD outperformed the Gabor and also other state-of-art Gabor techniques.

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.