Fractal descriptors based on the probability dimension: a texture analysis and classification approach
This work addresses texture analysis for image classification, but it appears incremental as it builds on existing fractal methods with a new descriptor approach.
The authors tackled the problem of describing and classifying gray-level texture images by proposing a novel technique that uses fractal descriptors based on the probability dimension, and they verified its effectiveness in classification tasks on benchmark datasets, demonstrating efficiency in discrimination.
In this work, we propose a novel technique for obtaining descriptors of gray-level texture images. The descriptors are provided by applying a multiscale transform to the fractal dimension of the image estimated through the probability (Voss) method. The effectiveness of the descriptors is verified in a classification task using benchmark over texture datasets. The results obtained demonstrate the efficiency of the proposed method as a tool for the description and discrimination of texture images.