Evaluation of robustness against rotation of LBP, CCR and ILBP features in granite texture classification

The aim of the paper is to conduct a performance evaluation where several texture descriptors such as Local Binary Patterns (LBP), Coordinated Clusters Representation (CCR) and (Improved Local Binary Patterns) ILBP are applied for granite texture classification. In our work we were particularly interested to assess the robustness of the analysed texture descriptors to image rotation when they were implemented in both the standard and rotation-invariant forms. In order to attain this goal, we have generated a database of granite textures that were rotated using hardware and software procedures. The experimental data indicate that the ILBP features return improved performance when compared with those achieved by the LBP and CCR descriptors. Another important finding resulting from this investigation reveals that the classification results obtained when the texture analysis techniques were applied to granite image data rotated by software procedures are inconsistent with those achieved when the hardware-rotated data are used for classification purposes. This discovery is surprising and suggests that the results obtained when the texture analysis techniques are evaluated on synthetically rotated data need to be interpreted with care, as the principal characteristics of the texture are altered by the data interpolation that is applied during the image rotation process.