Multiscale Fractal Descriptors Applied to Texture Classification
This is an incremental improvement for texture analysis in image processing.
The authors tackled texture classification by combining multiscale transforms with fractal descriptors to reduce noise, achieving a higher success rate and using fewer descriptors on the Brodatz dataset.
This work proposes the combination of multiscale transform with fractal descriptors employed in the classification of gray-level texture images. We apply the space-scale transform (derivative + Gaussian filter) over the Bouligand-Minkowski fractal descriptors, followed by a threshold over the filter response, aiming at attenuating noise effects caused by the final part of this response. The method is tested in the classification of a well-known data set (Brodatz) and compared with other classical texture descriptor techniques. The results demonstrate the advantage of the proposed approach, achieving a higher success rate with a reduced amount of descriptors.