Fractal Descriptors in the Fourier Domain Applied to Color Texture Analysis
This work addresses color texture analysis for image classification, representing an incremental advancement with a modest performance gain.
The authors tackled the problem of generating descriptors for color texture images by applying a linear color space transform and a multiscale fractal dimension approach based on Fourier transform, resulting in a nearly 3% accuracy improvement over the second-best method in classification tasks on two datasets.
The present work proposes the development of a novel method to provide descriptors for colored texture images. The method consists in two steps. In the first, we apply a linear transform in the color space of the image aiming at highlighting spatial structuring relations among the color of pixels. In a second moment, we apply a multiscale approach to the calculus of fractal dimension based on Fourier transform. From this multiscale operation, we extract the descriptors used to discriminate the texture represented in digital images. The accuracy of the method is verified in the classification of two color texture datasets, by comparing the performance of the proposed technique to other classical and state-of-the-art methods for color texture analysis. The results showed an advantage of almost 3% of the proposed technique over the second best approach.