Description of interest regions with local binary patterns

Marko Heikkila 1 Matti Pietikainen 1 Cordelia Schmid 2
2 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
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.
Type de document :
Article dans une revue
Pattern Recognition, Elsevier, 2009, 42 (3), pp.425-436. 〈10.1016/j.patcog.2008.08.014〉
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Soumis le : lundi 20 décembre 2010 - 10:24:15
Dernière modification le : lundi 17 décembre 2018 - 11:22:02

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



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