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 +

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available