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http://dx.doi.org/10.5626/JOK.2017.44.12.1313

Binary Visual Word Generation Techniques for A Fast Image Search  

Lee, Suwon (KAIST)
Publication Information
Journal of KIISE / v.44, no.12, 2017 , pp. 1313-1318 More about this Journal
Abstract
Aggregating local features in a single vector is a fundamental problem in an image search. In this process, the image search process can be speeded up if binary features which are extracted almost two order of magnitude faster than gradient-based features are utilized. However, in order to utilize the binary features in an image search, it is necessary to study the techniques for clustering binary features to generate binary visual words. This investigation is necessary because traditional clustering techniques for gradient-based features are not compatible with binary features. To this end, this paper studies the techniques for clustering binary features for the purpose of generating binary visual words. Through experiments, we analyze the trade-off between the accuracy and computational efficiency of an image search using binary features, and we then compare the proposed techniques. This research is expected to be applied to mobile applications, real-time applications, and web scale applications that require a fast image search.
Keywords
fast image search; binary feature; visual words; binary visual words;
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  • Reference
1 D. Lowe, “Distinctive image features from scaleinvariant keypoints,” International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004.   DOI
2 J. Sivic, and A. Zisserman, "Video Google: a text retrieval approach to object matching in videos," Proc. of the IEEE International Conference on Computer Vision 2003, pp. 1470-1477, 2003.
3 E. Rosten, and T. Drummond, "Machine learning for high-speed corner detection," Proc. of the European Conference on Computer Vision 2006, pp. 430-443, 2006.
4 M. Calonder, V. Lepetit, C. Strecha, and P. Fua, "BRIEF: binary robust independent elementary features," Proc. of the European Conference on Computer Vision 2010, pp. 5-11, 2010.
5 E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "ORB: an efficient alternative to SIFT or SURF," Proc. of the International Conference on Computer Vision 2011, pp. 2564-2571, 2011.
6 S. Leutenegger, M. Chli, and R. Siegwart, "BRISK: Binary robust invariant scalable keypoints," Proc. of the International Conference on Computer Vision 2011, pp. 2548-2555, 2011.
7 A. Alahi, R. Ortiz, and P. Vandergheynst, "FREAK: Fast Retina Keypoint," Proc. of the IEEE Conference on Computer Vision and Pattern Recognition 2012, pp. 510-517, 2012.
8 D. Arthur, and S. Vassilvitskii, "k-means++: the advantages of careful seeding," Proc. of the 18th annual ACM-SIAM symposium on Discrete algorithms 2007, pp. 1027-1035, 2007.
9 D. Nister, and H. Stewenius, "Scalable recognition with a vocabulary tree," Proc. of the IEEE Conference on Computer Vision and Pattern Recognition 2006, pp. 2161-2168, 2006.