Multi-dimensional local binary pattern descriptors for improved texture analysis

Texture analysis algorithms are employed in many computer vision applications. A group of high performing texture algorithms are based on the concept of local binary patterns (LBP) which describe the relationship of pixels to their local neighbourhood. LBP descriptors are invariant to intensity changes and rotation invariance is simple to derive. In addition, LBP features can be calculated for different neighbourhood radii and thus allow texture description at different scales. In conventional LBP methods, the histograms corresponding to different radii are simply concatenated which results in a loss of information between these scales and added ambiguity. In this paper, we address this problem and show that multi-dimensional LBP histograms provide effective texture descriptors. We demonstrate, on various texture datasets from the Outex suite and both for texture classification and texture retrieval scenarios, that our proposed approach consistently outperforms conventional LBP features.