skip to main content
10.1145/1180639.1180769acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
Article

Towards content-based relevance ranking for video search

Published: 23 October 2006 Publication History

Abstract

Most existing web video search engines index videos by file names, URLs, and surrounding texts. These types of video metadata roughly describe the whole video in an abstract level without taking the rich content, such as semantic content descriptions and speech within the video, into consideration. Therefore the relevance ranking of the video search results is not satisfactory as the details of video contents are ignored. In this paper we propose a novel relevance ranking approach for Web-based video search using both video metadata and the rich content contained in the videos. To leverage real content into ranking, the videos are segmented into shots, which are smaller and more semantic-meaningful retrievable units, and then more detailed information of video content such as semantic descriptions and speech of each shots are used to improve the retrieval and ranking performance. With video metadata and content information of shots, we developed an integrated ranking approach, which achieves improved ranking performance. We also introduce machine learning into the ranking system, and compare them with IR-model (information retrieval model) based method. The evaluation results demonstrate the effectiveness of the proposed ranking methods.

References

[1]
Hong-Jiang Zhang, A. Kankanhalli, and S. Smoliar, "Automatic Partitioning of Full-motion Video," A Guided Tour of Multimedia Systems and Applications, IEEE Computer Society Press, 1995.
[2]
http://www-nlpir.nist.gov/projects/trecvid
[3]
S. E. Robertson, S. Walker, and M. Beaulieu. Okapi at TREC-7: automatic ad hoc, filtering, VLC and filtering tracks. In Proceedings of TREC'99.
[4]
M. Naphade, J.R. Smith, F. Souvannavong, "On the Detection of Semantic Concepts at TRECVID," ACM Multimedia, ACM Press, New York, NY, pp. 660--667, Oct. 10-16, 2004
[5]
M. Naphade, L. Kennedy, J.R. Kender, S.F. Chang, J.R. Smith, P. Over, A. Hauptmann, "LSCOM-lite: A Light Scale Concept Ontology for Multimedia Understanding for TRECVID 2005," IBM Research Tech. Report, RC23612 (W0505-104), May, 2005.
[6]
Chris Burges, et.al, "Learning to Rank using Gradient Descent", ICML 2005, Bonn, Germany, pp.89-96, August 7-11, 2005.

Cited By

View all
  • (2011)A Human-Centered Computing Framework to Enable Personalized News Video RecommendationMultimedia Analysis, Processing and Communications10.1007/978-3-642-19551-8_18(475-495)Online publication date: 2011
  • (2010)A Human-Centered Computing Framework to Enable Personalized News Video RecommendationVideo Search and Mining10.1007/978-3-642-12900-1_10(261-281)Online publication date: 2010
  • (2009)Personalized News Video RecommendationProceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling10.1007/978-3-540-92892-8_46(459-471)Online publication date: 6-Jan-2009
  • Show More Cited By

Index Terms

  1. Towards content-based relevance ranking for video search

    Recommendations

    Comments

    Information & Contributors

    Information

    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]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 23 October 2006

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. content-based ranking
    2. relevance ranking
    3. video search

    Qualifiers

    • Article

    Conference

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

    Acceptance Rates

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

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)4
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 16 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2011)A Human-Centered Computing Framework to Enable Personalized News Video RecommendationMultimedia Analysis, Processing and Communications10.1007/978-3-642-19551-8_18(475-495)Online publication date: 2011
    • (2010)A Human-Centered Computing Framework to Enable Personalized News Video RecommendationVideo Search and Mining10.1007/978-3-642-12900-1_10(261-281)Online publication date: 2010
    • (2009)Personalized News Video RecommendationProceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling10.1007/978-3-540-92892-8_46(459-471)Online publication date: 6-Jan-2009
    • (2008)Personalized News Video Recommendation Via Interactive ExplorationProceedings of the 4th International Symposium on Advances in Visual Computing, Part II10.1007/978-3-540-89646-3_37(380-389)Online publication date: 1-Dec-2008
    • (2007)Image-Based Rendering for Computer Synthesized Human FiguresProceedings of the Second International Workshop on Semantic Media Adaptation and Personalization10.1109/SMAP.2007.22(229-232)Online publication date: 17-Dec-2007

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media