Combining different Local Binary Pattern variants to boost performance

This paper focuses on the combination of variants of local binary patterns (LBP), widely considered the state of the art among texture descriptors, using the same radius and the same number of neighborhoods. We report new experiments exploring several LBP-based descriptors and propose a set of variants for the representation of images. Our experiments are of two main types. In the first set, the Fourier transform is used to extract features starting from the histogram of uniform patterns. In these experiments we test different methods of extracting features from the histogram and each method is used to train a set of support vector machines (SVMs) which are then combined. In the second set of experiments, features are extracted from histograms using different definitions of uniform patterns. These are used to train SVMs, and the results are then combined. Our results show that descriptors extracted from LPB using the same radius and the same number of neighborhoods can be combined to improve classifier performance.