Pairwise rotation invariant co-occurrence local binary pattern

Authors:
Xianbiao Qi;Rong Xiao;Jun Guo;Lei Zhang
Affiliations:
Beijing University of Posts and Telecommunications, Beijing, P.R. China;Microsoft Research Asia, Beijing, P.R. China;Beijing University of Posts and Telecommunications, Beijing, P.R. China;Microsoft Research Asia, Beijing, P.R. China
Venue:
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Year:
2012

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Abstract

In this work, we introduce a novel pairwise rotation invariant co-occurrence local binary pattern (PRI-CoLBP) feature which incorporates two types of context - spatial co-occurrence and orientation co-occurrence. Different from traditional rotation invariant local features, pairwise rotation invariant co-occurrence features preserve relative angle between the orientations of individual features. The relative angle depicts the local curvature information, which is discriminative and rotation invariant. Experimental results on the CUReT, Brodatz, KTH-TIPS texture dataset, Flickr Material dataset, and Oxford 102 Flower dataset further demonstrate the superior performance of the proposed feature on texture classification, material recognition and flower recognition tasks.