Article

Video retrieval using high level features: exploiting query matching and confidence-based weighting

Authors:

Tat-Seng ChuaAuthors Info & Claims

CIVR'06: Proceedings of the 5th international conference on Image and Video Retrieval

Pages 143 - 152

https://doi.org/10.1007/11788034_15

Published: 13 July 2006 Publication History

Abstract

Recent research in video retrieval has focused on automated, high-level feature indexing on shots or frames. One important application of such indexing is to support precise video retrieval. We report on extensions of this semantic indexing on news video retrieval. First, we utilize extensive query analysis to relate various high-level features and query terms by matching the textual description and context in a time-dependent manner. Second, we introduce a framework to effectively fuse the relation weights with the detectors' confidence scores. This results in individual high level features that are weighted on a per-query basis. Tests on the TRECVID 2005 dataset show that the above two enhancements yield significant improvement in performance over a corresponding state-of-the-art video retrieval baseline.

References

[1]

Hauptmann, A., Chen, M.Y., Christel, M., Huang, C., Lin, W.H., Ng, T., Papernick, N., Velivelli, A., Yang, J., Yan, R., Yang, H., Wactlar, H.D.: Confounded expectations: Informedia at TRECVID 2004. In: TRECVID, 2004. (2004).

[2]

Miller, G.: Wordnet: An on-line lexical database. International Journal of Lexicography (1995).

[3]

Neo, S., Goh, H., Chua, T.: Multimodal event-based model for retrieval of multi-lingual news video. In: IWAIT. (2006).

[4]

Over, P., Ianeva, T.: TRECVID 2005: An introduction. In: TRECVID, 2005. (2005).

[5]

Smeaton, A.F., Kraaij, W., Over, P.: TRECVID - an overview. In: TRECVID, 2003. (2003).

[6]

Amir, A., Iyengar, G., Argillander, J., Campbell, M., Haubold, A., Ebadollahi, S., Kang, F., Naphade, M.R., Natsev, A.P., Smith, J.R., Tesic, J., Volkmer, T.: IBM research TRECVID- 2005 video retrieval system. In: TRECVID, 2005. (2005).

[7]

Chang, S.F., Hsu, W., Kennedy, L., Xie, L., Yanagawa, A., Zavesky, E., Zhang, D.Q.: Columbia university TRECVID-2005 video search and high-level feature extraction. In: TRECVID, 2005. (2005).

[8]

Snoek, C.G.M., van Gemert, J., Geusebroek, J.M., Huurnink, B., Koelma, D.C., Nguyen, G.P., de Rooij, O., Seinstra, F.J., Smeulders, A.W.M., Veenman, C.J., Worring, M.: The MediaMill TRECVID 2005 semantic video search engine. In: Proceedings of the 3rd TRECVID Workshop, NIST (2005).

[9]

Hauptmann, A.G., Christel, M., Concescu, R., Gao, J., Jin, Q., Lin, W.H., Pan, J.Y., Stevens, S.M., Yan, R., Yang, J., Zhang, Y.: CMU Informedia's TRECVID 2005 skirmishes. In: TRECVID, 2005. (2005).

[10]

Foley, C., Gurrin, C., Jones, G., Lee, H., McGivney, S., O'Connor, N.E., Sav, S., Smeaton, A.F., Wilkins, P.: TRECVid 2005 experiments at dublin city university. In: TRECVID, 2005. (2005).

[11]

Chua, T.S., Neo, S.Y., Goh, H.K., Zhao, M., Xiao, Y., Wang, G.: TRECVID 2005 by NUS PRIS. In: TRECVID 2005. (2005).

[12]

Chua, T.S., Neo, S.Y., Li, K., Wang, G., Shi, R., Zhao, M., Xu, H.: TRECVID 2004 search and feature extraction task by NUS PRIS. In: TRECVID 2004. (2004).

[13]

Yang, H., Chua, T.S., Wang, S., Koh, C.K.: Structured use of external knowledge for event-based open-domain question-answering. In: SIGIR 2003, Canada, Jul 2003. (2003).

[14]

Neo, S., Chua, T.: Query-dependent retrieval on news video. In: MMIR'05 workshop in SIGIR'05. (2005).

[15]

Resnik, P.: Semantic similarity in a taxonomy: An information- based measure and its applications to problems of ambiguity in natural language. Journal of Artificial Intelligence Research, 11 (1999) 95-130.

[16]

Kennedy, L.S., Natsev, A.P., Chang, S.F.: Automatic discover of query-class-dependent models for multimodal search. In: ACM Multimedia (MM '05). (2005) 882-891.

[17]

Christel, M.G., Hauptmann, A.G.: The use and utility of high-level semantic features in video retrieval. In: Conf. on Image and Video Retrieval, Singapore (2005) 134-144.

Cited By

Mettes PKoelma DSnoek C(2020)Shuffled ImageNet Banks for Video Event Detection and SearchACM Transactions on Multimedia Computing, Communications, and Applications10.1145/337787516:2(1-21)Online publication date: 22-May-2020
https://dl.acm.org/doi/10.1145/3377875
Mazaheri AGong BShah M(2018)Learning a Multi-Concept Video Retrieval Model with Multiple Latent VariablesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/317664714:2(1-21)Online publication date: 25-Apr-2018
https://dl.acm.org/doi/10.1145/3176647
Boer MLu YZhang HSchutte KNgo CKraaij W(2017)Semantic Reasoning in Zero Example Video Event RetrievalACM Transactions on Multimedia Computing, Communications, and Applications10.1145/313128813:4(1-17)Online publication date: 4-Oct-2017
https://dl.acm.org/doi/10.1145/3131288
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Video retrieval using high level features: exploiting query matching and confidence-based weighting
1. Information systems
  1. Information retrieval

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Published In

cover image Guide Proceedings

CIVR'06: Proceedings of the 5th international conference on Image and Video Retrieval

July 2006

546 pages

ISBN:3540360182

Editors:
Hari Sundaram
Arts, Media and Engineering Program, Arizona State University, Tempe, AZ
,
Milind Naphade
Intelligent Information Management Department, IBM T.J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY
,
John R. Smith
Intelligent Information Management Department, IBM T. J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY
,
Yong Rui
Microsoft Corporation, Microsoft China R&D Group, 49 Zhichun Road, Beijing, NY, China

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 13 July 2006

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Cited By

Mettes PKoelma DSnoek C(2020)Shuffled ImageNet Banks for Video Event Detection and SearchACM Transactions on Multimedia Computing, Communications, and Applications10.1145/337787516:2(1-21)Online publication date: 22-May-2020
Mazaheri AGong BShah M(2018)Learning a Multi-Concept Video Retrieval Model with Multiple Latent VariablesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/317664714:2(1-21)Online publication date: 25-Apr-2018
Boer MLu YZhang HSchutte KNgo CKraaij W(2017)Semantic Reasoning in Zero Example Video Event RetrievalACM Transactions on Multimedia Computing, Communications, and Applications10.1145/313128813:4(1-17)Online publication date: 4-Oct-2017
Afzal MWu XChen HJiang YPeng Q(2016)Web video categorization using category-predictive classifiers and category-specific concept classifiersNeurocomputing10.1016/j.neucom.2016.06.004214:C(175-190)Online publication date: 19-Nov-2016
Habibian AMensink TSnoek CKankanhalli MRueger SManmatha RJose Jvan Rijsbergen K(2014)Composite Concept Discovery for Zero-Shot Video Event DetectionProceedings of International Conference on Multimedia Retrieval10.1145/2578726.2578746(17-24)Online publication date: 1-Apr-2014
Yuan JZhao YLuan HWang MChua T(2014)Memory recall based video searchACM Transactions on Multimedia Computing, Communications, and Applications10.1145/253440910:2(1-21)Online publication date: 14-Feb-2014
Long BChang Y(2014)Relevance Ranking for Vertical Search EnginesundefinedOnline publication date: 14-Feb-2014
Dalton JAllan JMirajkar PHe QIyengar ANejdl WPei JRastogi R(2013)Zero-shot video retrieval using content and conceptsProceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2507880(1857-1860)Online publication date: 27-Oct-2013
Yuan JZha ZZheng YWang MZhou XChua TCandan KPanchanathan SPrabhakaran BSundaram HFeng WSebe N(2011)Learning concept bundles for video search with complex queriesProceedings of the 19th ACM international conference on Multimedia10.1145/2072298.2072357(453-462)Online publication date: 28-Nov-2011
Wei XYang ZCandan KPanchanathan SPrabhakaran BSundaram HFeng WSebe N(2011)Coached active learning for interactive video searchProceedings of the 19th ACM international conference on Multimedia10.1145/2072298.2072356(443-452)Online publication date: 28-Nov-2011
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