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Journal Articles Pattern Recognition Year : 2009

Description of interest regions with local binary patterns

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Abstract

This paper presents a novel method for interest region description. We adopted the idea that the appearance of an interest region can be well characterized by the distribution of its local features. The most well-known descriptor built on this idea is the SIFT descriptor that uses gradient as the local feature. Thus far, existing texture features are not widely utilized in the context of region description. In this paper, we introduce a new texture feature called center-symmetric local binary pattern (CS-LBP) that is a modified version of the well-known local binary pattern (LBP) feature. To combine the strengths of the SIFT and LBP, we use the CS-LBP as the local feature in the SIFT algorithm. The resulting descriptor is called the CS-LBP descriptor. In the matching and object category classification experiments, our descriptor performs favorably compared to the SIFT. Furthermore, the CS-LBP descriptor is computationally simpler than the SIFT.
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Dates and versions

inria-00548650 , version 1 (20-12-2010)

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Marko Heikkila, Matti Pietikainen, Cordelia Schmid. Description of interest regions with local binary patterns. Pattern Recognition, 2009, 42 (3), pp.425-436. ⟨10.1016/j.patcog.2008.08.014⟩. ⟨inria-00548650⟩
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