Survey on LBP based texture descriptors for image classification


The aim of this work is to find the best way for describing a given texture using a Local Binary Pattern (LBP) based approach. First several different approaches are compared, then the best fusion approach is tested on different datasets and compared with several approaches proposed in the literature (for fair comparisons, when possible we have used code shared by the original authors).

Our experiments show that a fusion approach based on uniform Local Quinary Pattern (LQP) and a rotation invariant Local Quinary Pattern, where a bin selection based on variance is performed and Neighborhood Preserving Embedding (NPE) feature transform is applied, obtains a method that performs well on all tested datasets.

As the classifier, we have tested a stand-alone support vector machine (SVM) and a random subspace ensemble of SVM. We compare several texture descriptors and show that our proposed approach coupled with random subspace ensemble outperforms other recent state-of-the-art approaches. This conclusion is based on extensive experiments conducted in several domains using six benchmark databases.

Keywords texture descriptors; local binary patterns; local quinary patterns; support vector machines; random subspace

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