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Study on the combination of video concept detectors

Published: 26 October 2008 Publication History

Abstract

This paper studies the combination of video concept detectors with a labeled fusion set. We point out that the computational cost of the grid search for fusion weights increases exponentially with the number of detectors, and it is thus infeasible when dealing with a large number of detectors. To avoid the difficulty, we adopt incremental fusion approach, i.e., in each round two detectors are combined and hence only 1-dimensional grid search is needed. We propose a Bottom-Up Incremental Fusion (BUIF) method which keeps selecting the detectors with lowest performance for combination. We conduct experiments on TRECVID benchmark dataset for 39 concepts with 38 detection methods. Ten different fusion strategies are compared, and empirical results have demonstrated the superiority of the proposed incremental fusion approach.

References

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TRECVID: TREC video retrieval evaluation, http://www-nlpir.nist.gov/projects/trecvid.
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L. Chen, D. Ding, D. Wang, F. Lin, and B. Zhang. AP-based Borda voting method for feature extraction in TRECVID 2004. In Proceedings of ECIR, 2005.
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Cited By

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  • (2016)Intermediate Semantics Based Distance Metric Learning for Video Annotation and Similarity MeasurementsWeb Information Systems Engineering – WISE 201610.1007/978-3-319-48740-3_16(227-242)Online publication date: 2-Nov-2016
  • (2012)Social image annotation via cross-domain subspace learningMultimedia Tools and Applications10.1007/s11042-010-0567-256:1(91-108)Online publication date: 1-Jan-2012
  • (2010)Metric learning with feature decomposition for image categorizationNeurocomputing10.1016/j.neucom.2009.08.02373:10-12(1562-1569)Online publication date: Jun-2010

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cover image ACM Conferences
MM '08: Proceedings of the 16th ACM international conference on Multimedia
October 2008
1206 pages
ISBN:9781605583037
DOI:10.1145/1459359
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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Publication History

Published: 26 October 2008

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Author Tags

  1. fusion
  2. video concept detection

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  • Short-paper

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MM08
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MM08: ACM Multimedia Conference 2008
October 26 - 31, 2008
British Columbia, Vancouver, Canada

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

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Cited By

View all
  • (2016)Intermediate Semantics Based Distance Metric Learning for Video Annotation and Similarity MeasurementsWeb Information Systems Engineering – WISE 201610.1007/978-3-319-48740-3_16(227-242)Online publication date: 2-Nov-2016
  • (2012)Social image annotation via cross-domain subspace learningMultimedia Tools and Applications10.1007/s11042-010-0567-256:1(91-108)Online publication date: 1-Jan-2012
  • (2010)Metric learning with feature decomposition for image categorizationNeurocomputing10.1016/j.neucom.2009.08.02373:10-12(1562-1569)Online publication date: Jun-2010

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