Orthogonal design for scale invariant feature transform optimization

Xintao Ding; Yonglong Luo; Yunyun Yi; Biao Jie; Taochun Wang; Weixin Bian

doi:10.1117/1.JEI.25.5.053030

17 October 2016 Orthogonal design for scale invariant feature transform optimization

Xintao Ding, Yonglong Luo, Yunyun Yi, Biao Jie, Taochun Wang, Weixin Bian

Author Affiliations +

Funded by: Natural Science Foundation of China, Natural Science Foundation of Anhui Province (Anhui Provincial Natural Science Foundation), Open Projects Program of National Laboratory of Pattern Recognition

Journal of Electronic Imaging, Vol. 25, Issue 5, 053030 (October 2016). https://doi.org/10.1117/1.JEI.25.5.053030

Abstract

To improve object recognition capabilities in applications, we used orthogonal design (OD) to choose a group of optimal parameters in the parameter space of scale invariant feature transform (SIFT). In the case of global optimization (GOP) and local optimization (LOP) objectives, our aim is to show the operation of OD on the SIFT method. The GOP aims to increase the number of correctly detected true matches (NoCDTM) and the ratio of NoCDTM to all matches. In contrast, the LOP mainly aims to increase the performance of recall–precision. In detail, we first abstracted the SIFT method to a 9-way fixed-effect model with an interaction. Second, we designed a mixed orthogonal array, MA(64,2³4²⁰,2), and its header table to optimize the SIFT parameters. Finally, two groups of parameters were obtained for GOP and LOP after orthogonal experiments and statistical analyses were implemented. Our experiments on four groups of data demonstrate that compared with the state-of-the-art methods, GOP can access more correct matches and is more effective against object recognition. In addition, LOP is favorable in terms of the recall–precision.

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Xintao Ding, Yonglong Luo, Yunyun Yi, Biao Jie, Taochun Wang, and Weixin Bian "Orthogonal design for scale invariant feature transform optimization," Journal of Electronic Imaging 25(5), 053030 (17 October 2016). https://doi.org/10.1117/1.JEI.25.5.053030

Published: 17 October 2016

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