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Orthogonal design for scale invariant feature transform optimization

[+] Author Affiliations
Xintao Ding

Anhui Normal University, School of Territorial Resources and Tourism, No. 189, South Jiuhua Road, Wuhu 241003, China

Anhui Normal University, School of Mathematics and Computer Science, No. 189, South Jiuhua Road, Wuhu 241003, China

Yonglong Luo, Biao Jie, Taochun Wang, Weixin Bian

Anhui Normal University, School of Mathematics and Computer Science, No. 189, South Jiuhua Road, Wuhu 241003, China

Yunyun Yi

Anhui Polytechnic University, Department of Computer Science, No. 8, Middle Beijing Road, Wuhu 241000, China

J. Electron. Imaging. 25(5), 053030 (Oct 17, 2016). doi:10.1117/1.JEI.25.5.053030
History: Received March 20, 2016; Accepted September 15, 2016
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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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Citation

Xintao Ding ; Yonglong Luo ; Yunyun Yi ; Biao Jie ; Taochun Wang, et al.
"Orthogonal design for scale invariant feature transform optimization", J. Electron. Imaging. 25(5), 053030 (Oct 17, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.5.053030


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