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Learning moment-based fast local binary descriptor

[+] Author Affiliations
Abdelkader Bellarbi

University of Evry, Informatique Biologie Intégrative et Systèmes Complexes (IBISC) Laboratory, Evry, France

Centre de Développement des Technologies Avancées (CDTA), Robotic Department, Algiers, Algeria

Nadia Zenati, Hayet Belghit

Centre de Développement des Technologies Avancées (CDTA), Robotic Department, Algiers, Algeria

Samir Otmane

University of Evry, Informatique Biologie Intégrative et Systèmes Complexes (IBISC) Laboratory, Evry, France

J. Electron. Imaging. 26(2), 023006 (Mar 16, 2017). doi:10.1117/1.JEI.26.2.023006
History: Received August 17, 2016; Accepted February 17, 2017
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Abstract.  Recently, binary descriptors have attracted significant attention due to their speed and low memory consumption; however, using intensity differences to calculate the binary descriptive vector is not efficient enough. We propose an approach to binary description called POLAR_MOBIL, in which we perform binary tests between geometrical and statistical information using moments in the patch instead of the classical intensity binary test. In addition, we introduce a learning technique used to select an optimized set of binary tests with low correlation and high variance. This approach offers high distinctiveness against affine transformations and appearance changes. An extensive evaluation on well-known benchmark datasets reveals the robustness and the effectiveness of the proposed descriptor, as well as its good performance in terms of low computation complexity when compared with state-of-the-art real-time local descriptors.

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© 2017 SPIE and IS&T

Citation

Abdelkader Bellarbi ; Nadia Zenati ; Samir Otmane and Hayet Belghit
"Learning moment-based fast local binary descriptor", J. Electron. Imaging. 26(2), 023006 (Mar 16, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.2.023006


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