  Local Binary Pattern (LBP) is a traditional descriptor for texture analysis
that gained attention in the last decade. Being robust to several properties
such as invariance to illumination translation and scaling, LBPs achieved
state-of-the-art results in several applications. However, LBPs are not able to
capture high-level features from the image, merely encoding features with low
abstraction levels. In this work, we propose Deep LBP, which borrow ideas from
the deep learning community to improve LBP expressiveness. By using
parametrized data-driven LBP, we enable successive applications of the LBP
operators with increasing abstraction levels. We validate the relevance of the
proposed idea in several datasets from a wide range of applications. Deep LBP
improved the performance of traditional and multiscale LBP in all cases.
