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Proceedings Paper

Learning to assign binary weights to binary descriptor
Author(s): Zhoudi Huang; Zhenzhong Wei; Guangjun Zhang
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Paper Abstract

Constructing robust binary local feature descriptors are receiving increasing interest due to their binary nature, which can enable fast processing while requiring significantly less memory than their floating-point competitors. To bridge the performance gap between the binary and floating-point descriptors without increasing the computational cost of computing and matching, optimal binary weights are learning to assign to binary descriptor for considering each bit might contribute differently to the distinctiveness and robustness. Technically, a large-scale regularized optimization method is applied to learn float weights for each bit of the binary descriptor. Furthermore, binary approximation for the float weights is performed by utilizing an efficient alternatively greedy strategy, which can significantly improve the discriminative power while preserve fast matching advantage. Extensive experimental results on two challenging datasets (Brown dataset and Oxford dataset) demonstrate the effectiveness and efficiency of the proposed method.

Paper Details

Date Published: 1 November 2016
PDF: 8 pages
Proc. SPIE 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control, 1015721 (1 November 2016); doi: 10.1117/12.2246737
Show Author Affiliations
Zhoudi Huang, Beihang Univ. (China)
Zhenzhong Wei, Beihang Univ. (China)
Guangjun Zhang, Beihang Univ. (China)


Published in SPIE Proceedings Vol. 10157:
Infrared Technology and Applications, and Robot Sensing and Advanced Control

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