Article

Analysis of vector space model and spatiotemporal segmentation for video indexing and retrieval

Authors:

Eric Galmar,

Benoit HuetAuthors Info & Claims

CIVR '07: Proceedings of the 6th ACM international conference on Image and video retrieval

Pages 433 - 440

https://doi.org/10.1145/1282280.1282344

Published: 09 July 2007 Publication History

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Abstract

Region-based video indexing systems have opened up new possibilities for the description of visual content. However, these systems are affected by spatial variations on the regions obtained from image segmentation algorithms and by the complexity of region matching techniques. In this paper, we propose to enhance these systems with the use of spatiotemporal regions. The indexing framework studied for that purpose is based on the Vector Space Model (VSM), which enables efficient and compact shot representation with count vectors. We analyse the properties of the VSM and show that shot description can be improved by considering spatio temporal representations. For evaluation, we further compare the performance of the system using the spatiotemporal and the keyframe approach. Experimental results show that the spatiotemporal approach is advantageous in terms of retrieval performance and robustness of the description.

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Cited By

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Bellamine ITairi H(2015)Motion detection using color structure-texture image decomposition2015 Intelligent Systems and Computer Vision (ISCV)10.1109/ISACV.2015.7105545(1-8)Online publication date: Mar-2015
https://doi.org/10.1109/ISACV.2015.7105545
Bellamine ITairi H(2013)Detecting motion using the Structure-Texture Image Decomposition and Space-Time Interest Points2013 8th International Conference on Intelligent Systems: Theories and Applications (SITA)10.1109/SITA.2013.6560818(1-7)Online publication date: May-2013
https://doi.org/10.1109/SITA.2013.6560818
Bellamine ITairi H(2013)Motion detection and tracking using space-time interest points2013 ACS International Conference on Computer Systems and Applications (AICCSA)10.1109/AICCSA.2013.6616419(1-7)Online publication date: May-2013
https://doi.org/10.1109/AICCSA.2013.6616419
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Index Terms

Analysis of vector space model and spatiotemporal segmentation for video indexing and retrieval
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding
2. Information systems
  1. Information retrieval
    1. Document representation
    2. Search engine architectures and scalability
      1. Search engine indexing

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Published In

CIVR '07: Proceedings of the 6th ACM international conference on Image and video retrieval

July 2007

655 pages

ISBN:9781595937339

DOI:10.1145/1282280

General Chairs:
Nicu Sebe
Univ. of Amsterdam, The Netherlands
,
Marcel Worring
Univ. of Amsterdam, The Netherlands

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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 July 2007

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CIVR07: International Conference on Image and Video Retrieval 2007

July 9 - 11, 2007

Amsterdam, The Netherlands

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Cited By

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Bellamine ITairi H(2015)Motion detection using color structure-texture image decomposition2015 Intelligent Systems and Computer Vision (ISCV)10.1109/ISACV.2015.7105545(1-8)Online publication date: Mar-2015
https://doi.org/10.1109/ISACV.2015.7105545
Bellamine ITairi H(2013)Detecting motion using the Structure-Texture Image Decomposition and Space-Time Interest Points2013 8th International Conference on Intelligent Systems: Theories and Applications (SITA)10.1109/SITA.2013.6560818(1-7)Online publication date: May-2013
https://doi.org/10.1109/SITA.2013.6560818
Bellamine ITairi H(2013)Motion detection and tracking using space-time interest points2013 ACS International Conference on Computer Systems and Applications (AICCSA)10.1109/AICCSA.2013.6616419(1-7)Online publication date: May-2013
https://doi.org/10.1109/AICCSA.2013.6616419
Jiang WCotton CChang SEllis DLoui A(2010)Audio-visual atoms for generic video concept classificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/1823746.18237486:3(1-19)Online publication date: 27-Aug-2010
https://dl.acm.org/doi/10.1145/1823746.1823748
Brendel WTodorovic S(2009)Video object segmentation by tracking regions2009 IEEE 12th International Conference on Computer Vision10.1109/ICCV.2009.5459242(833-840)Online publication date: Sep-2009
https://doi.org/10.1109/ICCV.2009.5459242
Paleari MBenmokhtar RHuet B(2009)Evidence Theory-Based Multimodal Emotion RecognitionProceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling10.1007/978-3-540-92892-8_44(435-446)Online publication date: 6-Jan-2009
https://dl.acm.org/doi/10.1007/978-3-540-92892-8_44
Paleari MHuet B(2008)Toward emotion indexing of multimedia excerpts2008 International Workshop on Content-Based Multimedia Indexing10.1109/CBMI.2008.4564978(425-432)Online publication date: Jun-2008
https://doi.org/10.1109/CBMI.2008.4564978

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Improvement of vector space information retrieval model based on supervised learning

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