Gabor wavelets combined with volumetric fractal dimension applied to texture analysis

TL;DR

This paper introduces a novel texture analysis method combining Gabor wavelets with volumetric fractal dimension to improve feature extraction and classification accuracy in computer vision tasks.

Contribution

The paper proposes a new approach that enhances Gabor wavelet descriptors by incorporating fractal signatures, leading to more effective texture feature representation.

Findings

Outperforms existing methods in texture classification accuracy

Provides more detailed and reliable texture features

Demonstrates effectiveness across multiple texture databases

Abstract

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.