Fractal Descriptors of Texture Images Based on the Triangular Prism Dimension
This work addresses texture analysis for image classification and retrieval, offering a method that improves performance in practical applications, though it appears incremental as it builds on fractal geometry with specific modifications.
The authors tackled texture image analysis by introducing a novel fractal descriptor based on triangular prism dimension, achieving results that outperform classical and state-of-the-art methods on Brodatz and Vistex datasets for classification and retrieval tasks.
This work presents a novel descriptor for texture images based on fractal geometry and its application to image analysis. The descriptors are provided by estimating the triangular prism fractal dimension under different scales with a weight exponential parameter, followed by dimensionality reduction using Karhunen-Loève transform. The efficiency of the proposed descriptors is tested on two well-known texture data sets, that is, Brodatz and Vistex, both for classification and image retrieval. The novel method is also tested concerning invariances in situations when the textures are rotated or affected by Gaussian noise. The obtained results outperform other classical and state-of-the-art descriptors in the literature and demonstrate the power of the triangular descriptors in these tasks, suggesting their use in practical applications of image analysis based on texture features.