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Fusing semantics, observability, reliability and diversity of concept detectors for video search

Published: 26 October 2008 Publication History

Abstract

Effective utilization of semantic concept detectors for large-scale video search has recently become a topic of intensive studies. One of main challenges is the selection and fusion of appropriate detectors, which considers not only semantics but also the reliability of detectors, observability and diversity of detectors in target video domains. In this paper, we present a novel fusion technique which considers different aspects of detectors for query answering. In addition to utilizing detectors for bridging the semantic gap of user queries and multimedia data, we also address the issue of "observability gap" among detectors which could not be directly inferred from semantic reasoning such as using ontology. To facilitate the selection of detectors, we propose the building of two vector spaces: semantic space (SS) and observability space (OS). We categorize the set of detectors selected separately from SS and OS into four types: anchor, bridge, positive and negative concepts. A multi-level fusion strategy is proposed to novelly combine detectors, allowing the enhancement of detector reliability while enabling the observability, semantics and diversity of concepts being utilized for query answering. By experimenting the proposed approach on TRECVID 2005-2007 datasets and queries, we demonstrate the significance of considering observability, reliability and diversity, in addition to the semantics of detectors to queries.

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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
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    Publication History

    Published: 26 October 2008

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    Author Tags

    1. concept-based video search
    2. detector selection and fusion

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

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    • (2024)A Picture Is Worth a Graph: A Blueprint Debate Paradigm for Multimodal ReasoningProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681102(419-428)Online publication date: 28-Oct-2024
    • (2024)Generative Active Learning for Image Synthesis PersonalizationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680773(10669-10677)Online publication date: 28-Oct-2024
    • (2013)Constructing and Utilizing Video Ontology for Accurate and Fast RetrievalMultimedia Data Engineering Applications and Processing10.4018/978-1-4666-2940-0.ch012(226-242)Online publication date: 2013
    • (2011)Effectiveness of video ontology in query by example approachProceedings of the 7th international conference on Active media technology10.5555/2033896.2033907(49-58)Online publication date: 7-Sep-2011
    • (2011)Constructing and Utilizing Video Ontology for Accurate and Fast RetrievalInternational Journal of Multimedia Data Engineering & Management10.4018/jmdem.20111001042:4(59-75)Online publication date: 1-Oct-2011
    • (2011)Learning concept bundles for video search with complex queriesProceedings of the 19th ACM international conference on Multimedia10.1145/2072298.2072357(453-462)Online publication date: 28-Nov-2011
    • (2011)Coached active learning for interactive video searchProceedings of the 19th ACM international conference on Multimedia10.1145/2072298.2072356(443-452)Online publication date: 28-Nov-2011
    • (2011)Graph-based multi-space semantic correlation propagation for video retrievalThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-010-0510-627:1(21-34)Online publication date: 1-Jan-2011
    • (2011)Effectiveness of Video Ontology in Query by Example ApproachActive Media Technology10.1007/978-3-642-23620-4_9(49-58)Online publication date: 2011
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