An Analysis of Monochrome Conversions and Normalizations on the Local Binary Patterns Texture Descriptors

While the importance of the choice of color space for color descriptors has been studied extensively, a similar study for image texture descriptors is missing. This publication investigates the effect of color-to-monochrome conversions, image normalization, and metrics on the discriminative power of texture descriptors. The measure of the discriminative power of a feature is formulated as supervised spectral feature analysis. This analysis allows to measure the relative performance of a feature under varying conditions as long as the feature dimension is maintained. Feature discrimination evaluation is applied to Local Binary Patterns texture descriptors and it is shown how the proposed metric directly maps to classification performance. Based on this metric, we demonstrate that the choice of color-to-monochrome conversion and normalization can have a significant effect on the performance of the LBP descriptors.