Enhancing texture descriptors by a neighborhood approach to the non-additive entropy

• Local non-additive entropy is used to enhance descriptors of texture images.
• The entropy enriches the descriptors by measuring the local information.
• The proposal improved the performance of all of the analyzed methods.
• The gain was more significant for the neighborhood-based descriptors.
• The proposal can be applied to other descriptors in real-world problems.

This work proposes to enhance well-known descriptors of texture images by extracting such descriptors both directly from pixel intensities as well as from the local non-additive entropy of the image. The method can be divided into four steps. 1) The descriptors are computed for the original image according to what is described in the literature. 2) The image is transformed by computing the non-additive entropy at each pixel, considering its neighborhood. 3) Similarly to step 1, the descriptors are computed from the transformed image. 4) Descriptors from the original and transformed images are combined by means of a Karhunen–Loève transform. Four texture descriptors widely used in the literature were considered: Gabor wavelets, Gray-Level Co-occurrence Matrix, Local Binary Patterns and Bouligand–Minkowski fractal descriptors. The proposal is assessed by comparing the performance of the descriptors alone and after combined with the non-additive entropy. The results demonstrate that the combination achieved the best results both in image retrieval and classification tasks. The entropy is still more efficient in local-based methods: Local Binary Patterns and Gray-Level Co-occurrence Matrix.