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This work presents a novel descriptor for texture images based on fractalgeometry and its application to image analysis. The descriptors are provided byestimating the triangular prism fractal dimension under different scales with aweight exponential parameter, followed by dimensionality reduction usingKarhunen-Lo\`{e}ve transform. The efficiency of the proposed descriptors istested on two well-known texture data sets, that is, Brodatz and Vistex, bothfor classification and image retrieval. The novel method is also testedconcerning invariances in situations when the textures are rotated or affectedby Gaussian noise. The obtained results outperform other classical andstate-of-the-art descriptors in the literature and demonstrate the power of thetriangular descriptors in these tasks, suggesting their use in practicalapplications of image analysis based on texture features.

Fractal Descriptors of Texture Images Based on the Triangular Prism Dimension