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Issue No.02 - April-June (2013 vol.20)
pp: 62-71
Xin Xin , Samsung Research America and Northwestern University
Abhishek Nagar , Samsung Research America
Gaurav Srivastava , Samsung Research America
Zhu Li , Samsung Research America
Felix Fernandes , Samsung Research America
Aggelos K. Katsaggelos , Northwestern University
Visual search over large image repositories in real time is one of the key challenges for applications such as mobile visual query-by-capture, augmented reality, and biometrics-based identification. Search accuracy and response speed are two important performance factors. This article focuses on one of the important elements of this technology that enables large-scale visual search: indexing (or hashing). Indexing is the process of organizing a database of searchable elements into an efficiently searchable configuration. The searchable elements in our case are compact features extracted from images. This article explores a new indexing scheme. The authors optimize the design of a hash-code collision and counting scheme to enable fast search of visual features of MPEG CDVS.
Real time systems, Visualization, Search methods, LIbraries, Augmented reality, Biometrics, Image processing, Query processing, CDVS, multimedia, multimedia applications, large-scale visual search, indexing, hash-code collision, mobile search, MPEG
Xin Xin, Abhishek Nagar, Gaurav Srivastava, Zhu Li, Felix Fernandes, Aggelos K. Katsaggelos, "Large Visual Repository Search with Hash Collision Design Optimization", IEEE MultiMedia, vol.20, no. 2, pp. 62-71, April-June 2013, doi:10.1109/MMUL.2013.22
1. M. Funamizu, "Future of Internet Search: Mobile Version," Feb. 2008; http://petitinvention. 02/10future-of-internet- search-mobile-version.
2. Y.A. Reznik, "On MPEG Work Towards a Standard for Visual Search," Proc. SPIE 8135, Applications of Digital Image Processing XXXIV, SPIE, 2011.
3. M. Bober et al., "Test Model 5: Compact Descriptors for Visual Search," ISO/IEC JTC1/SC29/WG11/ W13335, Jan. 2013.
4. D.G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," Int'l J. Computer Vision, vol. 60, no. 2, 2004, pp. 91–110.
5. G. Csurka et al., "Visual Categorization with Bags of Keypoints," Proc. Workshop on Statistical Learning in Computer Vision, ECCV, vol. 1, 2004, p. 22.
6. J. Sivic and A. Zisserman, "Video Google: A Text Retrieval Approach to Object Matching in Videos," Proc. 9th IEEE Int'l Conf. Computer Vision, IEEE CS, 2003, pp. 1470–1477.
7. F. Perronnin et al., "Large-Scale Image Retrieval with Compressed Fisher Vectors," Proc. IEEE Conf., Computer Vision and Pattern Recognition (CVPR), IEEE CS Press, 2010, pp. 3384–3391.
8. F. Hao, J. Daugman, and P. Zielinski, "A Fast Search Algorithm for a Large Fuzzy Database," IEEE Trans. Information Forensics and Security, vol. 3, no. 2, 2008, pp. 203–212.
9. M.M. Esmaeili, R.K. Ward, and M. Fatourechi, "A Fast Approximate Nearest Neighbor Search Algorithm in the Hamming Space," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 34, no. 12, 2012, pp. 2481–2488.
10. J. Wang, S. Kumar, and S.-F. Chang, "Sequential Projection Learning for Hashing with Compact Codes," Proc. 27th Int'l Conf. Machine Learning, Omnipress, 2010, paper ID 178.
11. H. Xu et al., "Complementary Hashing for Approximate Nearest Neighbor Search," Proc. IEEE Int'l Conf. Computer Vision, IEEE CS, 2011, pp. 1631–1638.
12. X. Xin, Z. Li, and A.K. Katsaggelos, "Laplacian Embedding and Key Points Topology Verification for Large Scale Mobile Visual Identification," Signal Processing: Image Comm., Feb. 2013.
13. V. Chandrasekhar et al., "CHoG: Compressed Histogram of Gradients A Low Bit-Rate Feature Descriptor," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), IEEE CS, 2009, pp. 2504–2511.
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