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Carnegie Mellon University traditional informedia digital video retrieval system

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Michael G. ChristelAuthors Info & Claims

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

Page 647

https://doi.org/10.1145/1282280.1282374

Published: 09 July 2007 Publication History

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Abstract

The Carnegie Mellon University Informedia group has enjoyed consistent success with TRECVID interactive search using traditional storyboard interfaces for shot-based retrieval. For TRECVID 2006 the output of automatic search was included for the first time with storyboards, as an option for an interactive user. This access mechanism, query-by-best-of-topic, complements other access strategies, namely query-by-text (dominated by closed captioned text), query-by-image (primarily color-based syntactic lookup), and query-by-concept (using concept classifiers trained through machine learning techniques). The interface is tuned to support efficient performance required for TRECVID interactive search tasks where a maximum of 15 minutes can be spent per task. This 4-strategy Informedia interface scored significantly better than all other TRECVID 2006 interactive search systems. Interface attributes will be demonstrated in shot-based retrieval from news corpora.

References

[1]

Christel, M., and Yan, R. Merging Storyboard Strategies and Automatic Retrieval for Improving Interactive Video Search. In Proc. Image and Video Retrieval (CIVR) (July 2007).

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[2]

Snoek, C., Worring, M., Koelma, D., and Smeulders, A. A Learned Lexicon-Driven Paradigm for Interactive Video Retrieval. IEEE Trans. Multimedia 9(2), Feb. 2007, 280--292.

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Lokoč JBailer WBarthel KGurrin CHeller SJónsson BPeška LRossetto LSchoeffmann KVadicamo LVrochidis SWu J(2022)A Task Category Space for User-Centric Comparative Multimedia Search EvaluationsMultiMedia Modeling10.1007/978-3-030-98358-1_16(193-204)Online publication date: 15-Mar-2022
https://doi.org/10.1007/978-3-030-98358-1_16
Jiang WLoui ALei P(2012)A consumer video search system by audio-visual concept classification2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops10.1109/CVPRW.2012.6239341(39-44)Online publication date: Jun-2012
https://doi.org/10.1109/CVPRW.2012.6239341
Wang BSu JChou CTseng V(2011)Semantic Video Retrieval by Integrating Concept- and Content-Aware MiningProceedings of the 2011 International Conference on Technologies and Applications of Artificial Intelligence10.1109/TAAI.2011.14(32-37)Online publication date: 11-Nov-2011
https://dl.acm.org/doi/10.1109/TAAI.2011.14
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Index Terms

Carnegie Mellon University traditional informedia digital video retrieval system
1. Information systems
  1. Information systems applications
    1. Multimedia information systems

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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

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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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Lokoč JBailer WBarthel KGurrin CHeller SJónsson BPeška LRossetto LSchoeffmann KVadicamo LVrochidis SWu J(2022)A Task Category Space for User-Centric Comparative Multimedia Search EvaluationsMultiMedia Modeling10.1007/978-3-030-98358-1_16(193-204)Online publication date: 15-Mar-2022
https://doi.org/10.1007/978-3-030-98358-1_16
Jiang WLoui ALei P(2012)A consumer video search system by audio-visual concept classification2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops10.1109/CVPRW.2012.6239341(39-44)Online publication date: Jun-2012
https://doi.org/10.1109/CVPRW.2012.6239341
Wang BSu JChou CTseng V(2011)Semantic Video Retrieval by Integrating Concept- and Content-Aware MiningProceedings of the 2011 International Conference on Technologies and Applications of Artificial Intelligence10.1109/TAAI.2011.14(32-37)Online publication date: 11-Nov-2011
https://dl.acm.org/doi/10.1109/TAAI.2011.14
Zheng QZhou Z(2011)An MPEG-7 compatible video retrieval system with support for semantic queries2011 International Conference on Consumer Electronics, Communications and Networks (CECNet)10.1109/CECNET.2011.5768292(1035-1041)Online publication date: Apr-2011
https://doi.org/10.1109/CECNET.2011.5768292
Ma YMiller BCohen I(2007)Video sequence querying using clustering of objects' appearance modelsProceedings of the 3rd international conference on Advances in visual computing - Volume Part II10.5555/1779090.1779128(328-339)Online publication date: 26-Nov-2007
https://dl.acm.org/doi/10.5555/1779090.1779128
Ma YMiller BCohen I(2007)Video Sequence Querying Using Clustering of Objects’ Appearance ModelsAdvances in Visual Computing10.1007/978-3-540-76856-2_32(328-339)Online publication date: 2007
https://doi.org/10.1007/978-3-540-76856-2_32

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Merging storyboard strategies and automatic retrieval for improving interactive video search

Multimodal Video Retrieval with the 2017 IMOTION System

Addressing the challenge of visual information access from digital image and video libraries

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