Elena González

TL;DR: Comparing the performance of different visual features and classifiers over a set of 12 granite classes shows that classification based on colour and texture is highly effective and outperforms previous methods based on textural features alone.

TL;DR: An investigation into the robustness of the analysed texture descriptors to image rotation when they were implemented in both the standard and rotation-invariant forms finds that the ILBP features return improved performance when compared with those achieved by the LBP and CCR descriptors.

TL;DR: This paper presents a rotation-invariant CCR-based model for colour textures which yields a twofold improvement over the grayscale CCR, and shows robust invariance against rotation and a marked increase in classification accuracy with respect tograyscale versions of CCR and LBP.

TL;DR: Simple and compact colour descriptors, such as the mean of each colour channel, are as accurate as more complicated features and the use of simple statistical descriptors along with RGB data as the best practice to approach the problem is suggested.

TL;DR: The results show that DLBP provides a significant compression rate with only a slight accuracy decrease with respect to LBP, and that retaining information about the patterns' labels improves the discrimination capability of DLBP.