16 March 2017 Learning moment-based fast local binary descriptor
Author Affiliations +
J. of Electronic Imaging, 26(2), 023006 (2017). doi:10.1117/1.JEI.26.2.023006
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.
© 2017 SPIE and IS&T
Abdelkader Bellarbi, Nadia Zenati, Samir Otmane, Hayet Belghit, "Learning moment-based fast local binary descriptor," Journal of Electronic Imaging 26(2), 023006 (16 March 2017). https://doi.org/10.1117/1.JEI.26.2.023006 Submission: Received 17 August 2016; Accepted 17 February 2017
Submission: Received 17 August 2016; Accepted 17 February 2017


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