Browse > Article
http://dx.doi.org/10.5909/JEB.2012.17.2.388

Modified Speeded Up Robust Features(SURF) for Performance Enhancement of Mobile Visual Search System  

Seo, Jung-Jin (Department of Computer Science and Engineering, Konkuk University)
Yoona, Kyoung-Ro (Department of Computer Science and Engineering, Konkuk University)
Publication Information
Journal of Broadcast Engineering / v.17, no.2, 2012 , pp. 388-399 More about this Journal
Abstract
In the paper, we propose enhanced feature extraction and matching methods for a mobile environment based on modified SURF. We propose three methods to reduce the computational complexity in a mobile environment. The first is to reduce the dimensions of the SURF descriptor. We compare the performance of existing 64-dimensional SURF with several other dimensional SURFs. The second is to improve the performance using the sign of the trace of the Hessian matrix. In other words, feature points are considered as matched if they have the same sign for the trace of the Hessian matrix, otherwise considered not matched. The last one is to find the best distance-ratio which is used to determine the matching points. We find the best distance-ratio through experiments, and it gives the relatively high accuracy. Finally, existing system which is based on normal SURF method is compared with our proposed system which is based on these three proposed methods. We present that our proposed system shows reduced response time while preserving reasonably good matching accuracy.
Keywords
Visual search; SURF; Dimensional reduction; Laplacian sign; Distance ratio;
Citations & Related Records
연도 인용수 순위
  • Reference
1 OpenSURF, http://www.chrisevansdev.com/computer-vision-opensurf. html
2 Christopher Evans. Notes on the opensurf library. Technical Report CSTR-09-001, University of Bristol, 2009.
3 서정진, 윤경로, 휴대 단말의 위하여 개선된 Speeded Up Robust Features(SURF) 알고리듬의 성능 측정 및 분석, 한국방송공학회 추계학 술대회, 2011.
4 S. Nikolopoulos, S. G. Nikolov, I. Kompatsiaris, Study on Mobile Image Search, European Communities, 2010.
5 B . Girod, V. Chandrasekhar, D. M. Chen, N. M. Cheung, R. Grzeszczuk, Y. Reznik, G. Takacs, S. S. Tsai, and R. Vedantham, "Mobile visual search," IEEE Signal Processing Mag. (Special Issue on Mobile Media Search), vol. 28, no. 4, pp. 61-76, 2011.
6 D. Lowe, Distinctive image features from scale-invariant keypoints. IJCV, 60(2):90-110, 2004.
7 Xu Liu, Jonathan Hull, Jamey Graham, Jorge Moraleda, Timothee Bailloeul, Mobile Visual Search, Linking Printed Documents to Digital Media. In CVPR 2010 Demonstrations, 2010.
8 Compact Descriptors for Visual Search : Applications and Use Scenarios, N11529, MPEG output document, Geneva, CH, 2010.
9 정동석, [멀티미디어응용] MPEG-7의CDVS 국제표준화현황, 2010.
10 D. Lowe, Object recognition from local scale-invariant features, in : ICCV, 1999.
11 Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc J. Van Gool, SURF: Speeded up robust features, Computer Vision and Image Understanding (CVIU) 110 346-359, 2008.   DOI   ScienceOn
12 V. Chandrasekhar, D. M. Chen, A. Lin, G. Takacs, S. S. Tsai, N. M. Cheung, Y. Reznik, R. Grzeszczuk, and B. Girod, "Comparison of local feature descriptors for mobile visual search," in Proc. IEEE Int. Conf. Image Processing (ICIP), Hong Kong, 2010.
13 Euclidean distance, http://en.wikipedia.org/wiki/Euclidean_distance
14 Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. In: CVPR. Volume 2. 257-263, 2003.