Journal of University of Chinese Academy of Sciences >
Fast search method for large-scale video based on distributed LSH
Received date: 2012-01-04
Revised date: 2012-03-21
Online published: 2013-01-15
we propose a fast similarity-based video retrieval method for large-scale video database. This method solves scalability and efficiency problems. First, we extract feature vector set for every video and build index using a distributed memory hash structure, called MD-LSH. Secondly, we calculate the similarity of the relevant videos according to the returned similar frame sets. Finally, we return the sorted similar video list as the query results. Experiments show the effectiveness of the proposed method for large-scale video fast retrieval.
Key words： content-based retrieval; distributed LSH; large-scale video; fast search
CAO Hai-Bin , ZHU Ming , FENG Wei-Guo . Fast search method for large-scale video based on distributed LSH[J]. Journal of University of Chinese Academy of Sciences, 2013 , 30(1) : 106 -111 . DOI: 10.7523/j.issn.1002-1175.2013.01.016
[1] Shen H T, Liu J, Huang Z, et al. Near-duplicate video retrieval: current research and future trends[J]. Multimedia, IEEE, 2011, PP(99):1-10
[2] Chiu C Y, Wang H M. Time-series linear search for video copies based on compact signature manipulation and containment relation modeling[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2010, 20(11):1603-1613.
[3] Chiu C Y, Wang H M, Chen C S. Fast min-hashing indexing and robust spatio-temporal matching for detecting video copies[J]. ACM Transactions on Multimedia Computing, Communications, and Applications, 2010, 6(2):1-23.
[4] Lee S, Yoo C D. Robust video fingerprinting for content-based video identification[J]. IEEE Transactions on Circuits and Systems for Video Technology, July 2008, 18(7): 983-988.
[5] Schoeffmann K, Boeszoermenyi L. Video sequence identification in TV broadcasts[C]//Proceedings of the 17th International Conference on Advances in Multimedia Modeling. Taipei, Taiwan: Springer-Verlag, January 2011: Part I, 129-139.
[6] Wu X, Hauptmann A G, Ngo C W. Practical elimination of near-duplicates from web video search[C]//Proceedings of the 15th International Conference on Multimedia. New York, USA: ACM Press, Sep 2007:218-227.
[7] Zhang Z J, Cao C X, Zhang R J, et al. Video copy detection based on speeded up robust features and locality sensitive hashing[C]//Proceedings of IEEE International Conference on Automation and Logistics. Hong Kong and Macau: IEEE Press, Aug 2010:13-18.
[8] Indyk P, Motwani R. Approximate nearest neighbors: towards removing the curse of dimensionality[C]//Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing.New York, USA: ACM Press, 1998:604-613.
[9] Stupar A, Michel S, Schenkel R. RankReduce: processing k-nearest neighbor queries on top of MapReduce[C]//Proceedings of the 8th Workshop on Large-Scale Distributed Systems for Information Retrieval. Geneva, Switzerland: ACM Press, 2010,630:13-18.
[10] Dean J, Ghemawat S. Mapreduce: Simplified data processing on large clusters[J]. Communications of the ACM, 2008,51(1):107-113.
[11] Parisa H, Sebastian M, Karl A. Distributed similarity search in high dimensions using locality sensitive hashing[C]//Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology. New York, USA: ACM Press, 2009:744-755.
[12] Datar M, Immorlica N, Indyk P, et al. Locality-sensitive hashing scheme based on p-stable distributions[C]//Proceedings of the Twentieth Annual Symposium on Computational Geometry. New York, USA: ACM Press, 2004:253-262.
[13] Tang F, Lim S H, Chang N L, et al. A novel feature descriptor invariant to complex brightness changes[C]//Proceedings of IEEE Internationa1 Conference on Computer Vision and Pattern Recognition. Florida, USA: IEEE Press, 2009:2631-2638.
[14] Anand R, Jeffrey D U. Mining of massive datasets[M]. Cambridge: Cambridge University Press,2011:69-72.
/
| 〈 |
|
〉 |