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Efficient concept detection by fusing simple visual features

Published: 08 March 2009 Publication History

Abstract

Concept detection is one of the important tasks in video indexing due to its importance to bridging the semantic gap in multimedia retrieval. Many methods have been proposed for this task, however finding a method which can generalize well for a large number of concepts and is scalable for processing huge video databases is still challenging. In this paper, we introduce a general framework for efficient and scalable concept detection by fusing SVM classifiers trained by only simple visual features such as color moments, edge orientation histogram and local binary patterns. We evaluate the proposed framework for detecting a large number of concepts on various TRECVID datasets with hundreds of hours of video. Experimental results show that the proposed framework achieves good performance with a small computational cost.

References

[1]
S.-F. Chang et al. Recent advances and challenges of semantic image/video search. In Proc. ICASSP, 2007.
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B. Manjunath and W.-Y. Ma. Texture features for browsing and retrieval of image data. IEEE TPAMI, 18(8):837--842, Aug 1996.
[3]
T. Ojala et al. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE TPAMI, 24(7):971--987, Jul 2002.
[4]
D. Wang et al. Videodiver: Generic video indexing with diverse features. In Proc. ACM MIR, pages 61--70, 2007.
[5]
A. Yanagawa et al. Columbia University's baseline detectors for 374 LSCOM semantic visual concepts. Technical report, Columbia University, Mar 2007.

Cited By

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  • (2024)Concentric Blocking Techniques for Improved Feature Extraction in Local Binary Pattern (LBP) Systems2024 IEEE 9th International Conference on Adaptive Science and Technology (ICAST)10.1109/ICAST61769.2024.10856506(1-8)Online publication date: 24-Oct-2024
  • (2018)Concept Detection using Multiple Feature Set and Classifiers2018 International Conference on Advanced Computation and Telecommunication (ICACAT)10.1109/ICACAT.2018.8933574(1-7)Online publication date: Dec-2018

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cover image ACM Conferences
SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing
March 2009
2347 pages
ISBN:9781605581668
DOI:10.1145/1529282
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: 08 March 2009

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Author Tags

  1. TRECVID
  2. concept detection
  3. high level feature extraction

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  • Research-article

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SAC09
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SAC09: The 2009 ACM Symposium on Applied Computing
March 8, 2009 - March 12, 2008
Hawaii, Honolulu

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2024)Concentric Blocking Techniques for Improved Feature Extraction in Local Binary Pattern (LBP) Systems2024 IEEE 9th International Conference on Adaptive Science and Technology (ICAST)10.1109/ICAST61769.2024.10856506(1-8)Online publication date: 24-Oct-2024
  • (2018)Concept Detection using Multiple Feature Set and Classifiers2018 International Conference on Advanced Computation and Telecommunication (ICACAT)10.1109/ICACAT.2018.8933574(1-7)Online publication date: Dec-2018

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