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Explicit and implicit concept-based video retrieval with bipartite graph propagation model

Published: 25 October 2010 Publication History

Abstract

The major scientific problem for content-based video retrieval is the semantic gap. Generally speaking, there are two appropriate ways to bridge the semantic gap: the first one is from human perspective (top-down) and the other one is from computer perspective (bottom-up). The top-down method defines a concept lexicon from human perspective, trains the detector for each concept based on supervised learning, and then indexes the corpus with concept detectors. Since each concept has an explicit semantic meaning, we call this concept as an explicit concept. The bottom-up approach directly discovers the underlying latent topics from video corpus by machine perspective using an unsupervised learning. The video corpus is indexed subsequently by these latent topics. As opposite to explicit concepts, we name latent topics as implicit concepts. Given the explicit concept set is pre-defined and independent of the corpus, it is impossible to completely describe corpus and users' queries. On the other hand, the implicit concepts are dynamic and dependent on the corpus, which is able to fully describe corpus and users' queries. Therefore, combining explicit and implicit concepts could be a promising way to bridge the semantic gap effectively. In this paper, a Bipartite Graph Propagation Model (BGPM) is applied to automatically balance influences from explicit and implicit concepts. Concept nodes with strong connections to queries are reinforced no matter explicit or implicit. Demonstrated by the experiments on TREVID 2008 video dataset, BGPM successfully fuses explicit and implicit concepts to achieve a significant improvement on 48 search tasks.

References

[1]
C. G.M. Snoek and M. Worring. Concept-Based Video Retrieval. Foundations and Trends in Information Retrieval: 2(4), 215--322, 2009.
[2]
A. G. Hauptmann, et al. Video Retrieval Based on Semantic Concepts. Proceedings of the IEEE : 96(4), 602--622, 2008.
[3]
A. G. Hauptmann, R. Yan, W. H. Lin, and H. Wactlar. Can high-level concepts fill the semantic gap in video retrieval? IEEE Transactions on Multimedia: 9(5), 958--966, 2007.
[4]
Y.G. Jiang, C.W. Ngo, and S.F. Chang. Semantic Context Transfer across Heterogeneous Sources for Domain Adaptive.
[5]
X.G. Rui, M.J. Li, Z.W. Li, W.Y. Ma, Bipartite graph reinforcement model for web image annotation, In. Proc. of ACM Multimedia, 585--594, 2008.
[6]
D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3, 2003.
[7]
R. Zhao, et al. Narrowing the semantic gap-improved text-based web document retrieval using visual features. IEEE Transactions on Multimedia: 4(2), 189--200, 2002
[8]
J. Cao, Y.D. Zhang, B.L. Feng, X.F Hua, L. Bao, X. Zhang and J.T. Li. MCG-ICT-CAS TRECVID2008 Search Task Report. In. Proc. of TRECVID Workshop, 2008.
[9]
R. Yan, A. G. Hauptmann. Probabilistic Latent Query Analysis for Combining Multiple Retrieval Sources. In Proc. of ACM SIGIR, 324--331, 2006
[10]
Y.G. Jiang, A. Yanagawa, S.F. Chang, and C.W. Ngo. CU-VIREO374: Fusing Columbia374 and VIREO374 for Large Scale Semantic Concept Detection, Columbia University ADVENT Technical Report #223-2008-1, Aug. 2008.

Cited By

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  • (2016)High-level representation sketch for video event retrievalScience China Information Sciences10.1007/s11432-015-5494-459:7Online publication date: 15-Jun-2016
  • (2016)A cross-media distance metric learning framework based on multi-view correlation mining and matchingWorld Wide Web10.1007/s11280-015-0342-419:2(181-197)Online publication date: 1-Mar-2016
  • (2014)News Credibility Evaluation on Microblog with a Hierarchical Propagation ModelProceedings of the 2014 IEEE International Conference on Data Mining10.1109/ICDM.2014.91(230-239)Online publication date: 14-Dec-2014
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  1. Explicit and implicit concept-based video retrieval with bipartite graph propagation model

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    cover image ACM Conferences
    MM '10: Proceedings of the 18th ACM international conference on Multimedia
    October 2010
    1836 pages
    ISBN:9781605589336
    DOI:10.1145/1873951
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    Publication History

    Published: 25 October 2010

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    Author Tags

    1. bipartite graph
    2. explicit concept
    3. implicit concept
    4. semantic gap

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    MM '10
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    MM '10: ACM Multimedia Conference
    October 25 - 29, 2010
    Firenze, Italy

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    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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    Cited By

    View all
    • (2016)High-level representation sketch for video event retrievalScience China Information Sciences10.1007/s11432-015-5494-459:7Online publication date: 15-Jun-2016
    • (2016)A cross-media distance metric learning framework based on multi-view correlation mining and matchingWorld Wide Web10.1007/s11280-015-0342-419:2(181-197)Online publication date: 1-Mar-2016
    • (2014)News Credibility Evaluation on Microblog with a Hierarchical Propagation ModelProceedings of the 2014 IEEE International Conference on Data Mining10.1109/ICDM.2014.91(230-239)Online publication date: 14-Dec-2014
    • (2014)Multimedia event detection with multimodal feature fusion and temporal concept localizationMachine Vision and Applications10.1007/s00138-013-0525-x25:1(49-69)Online publication date: 1-Jan-2014
    • (2013)Beyond audio and video retrieval: topic-oriented multimedia summarizationInternational Journal of Multimedia Information Retrieval10.1007/s13735-012-0028-y2:2(131-144)Online publication date: 4-Jan-2013
    • (2012)Multimedia event recounting with concept based representationProceedings of the 20th ACM international conference on Multimedia10.1145/2393347.2396386(1073-1076)Online publication date: 29-Oct-2012
    • (2012)Beyond audio and video retrievalProceedings of the 2nd ACM International Conference on Multimedia Retrieval10.1145/2324796.2324799(1-8)Online publication date: 5-Jun-2012

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