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Manifold-ranking based video concept detection on large database and feature pool

Published: 23 October 2006 Publication History

Abstract

In this paper we discuss a typical case in video concept detection: to learn target concept using only a small number of positive samples. A novel manifold-ranking based scheme is proposed, which consists of three major components: feature pool construction, pre-filtering, and manifold-ranking. First, as there are large variations in the effective features for different concepts, a large feature pool is constructed, from which the most effective features can be selected automatically or semi-automatically. Second, to tackle the issue of large computation cost for successive manifold-ranking process when large video database is incorporated, we employ a pre-filtering process to filter out the majority of irrelevant samples while retaining the most relevant ones. And last, the manifold-ranking algorithm is used to explore the relationship among all of the rest samples based on the selected features. This scheme is extensible and flexible in terms of adding new features into the feature pool, introducing human interactions on selecting features, and defining new concepts.

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Cited By

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  • (2018)A Survey on Visual Content-Based Video Indexing and RetrievalIEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews10.1109/TSMCC.2011.210971041:6(797-819)Online publication date: 25-Dec-2018
  • (2018)Video Annotation Based on Kernel Linear Neighborhood PropagationIEEE Transactions on Multimedia10.1109/TMM.2008.92185310:4(620-628)Online publication date: 25-Dec-2018
  • (2018)Multigraph-based query-independent learning for video searchIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2009.202695119:12(1841-1850)Online publication date: 31-Dec-2018
  • Show More Cited By

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Published In

cover image ACM Conferences
MM '06: Proceedings of the 14th ACM international conference on Multimedia
October 2006
1072 pages
ISBN:1595934472
DOI:10.1145/1180639
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2006

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Author Tags

  1. manifold-ranking
  2. pre-filtering
  3. video concept detection

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MM06
MM06: The 14th ACM International Conference on Multimedia 2006
October 23 - 27, 2006
CA, Santa Barbara, USA

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

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Cited By

View all
  • (2018)A Survey on Visual Content-Based Video Indexing and RetrievalIEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews10.1109/TSMCC.2011.210971041:6(797-819)Online publication date: 25-Dec-2018
  • (2018)Video Annotation Based on Kernel Linear Neighborhood PropagationIEEE Transactions on Multimedia10.1109/TMM.2008.92185310:4(620-628)Online publication date: 25-Dec-2018
  • (2018)Multigraph-based query-independent learning for video searchIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2009.202695119:12(1841-1850)Online publication date: 31-Dec-2018
  • (2018)Typicality rankingMultimedia Tools and Applications10.1007/s11042-011-0892-070:2(647-660)Online publication date: 31-Dec-2018
  • (2018)Content based image retrieval via a transductive modelJournal of Intelligent Information Systems10.1007/s10844-013-0257-442:1(95-109)Online publication date: 28-Dec-2018
  • (2016)Social video annotation by combining features with a tri-adaptation approachMultimedia Systems10.1007/s00530-014-0405-x22:4(413-422)Online publication date: 1-Jul-2016
  • (2015)EMR: A Scalable Graph-Based Ranking Model for Content-Based Image RetrievalIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2013.7027:1(102-114)Online publication date: Jan-2015
  • (2015)Automatic image annotation using feature selection based on improving quantum particle swarm optimizationSignal Processing10.1016/j.sigpro.2014.10.031109:C(172-181)Online publication date: 1-Apr-2015
  • (2015)An efficient framework of Bregman divergence optimization for co-ranking images and tags in a heterogeneous networkMultimedia Tools and Applications10.1007/s11042-014-1873-x74:15(5635-5660)Online publication date: 1-Jul-2015
  • (2014)Scaling manifold ranking based image retrievalProceedings of the VLDB Endowment10.14778/2735496.27354988:4(341-352)Online publication date: 1-Dec-2014
  • Show More Cited By

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