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Concept-oriented video skimming via semantic video classification

Published: 10 October 2004 Publication History

Abstract

Effective video skimming requires a good understanding of the semantics of video contents. However, more existing systems for content-based video retrieval (CBVR) can only support low-level video analysis, but they have limited effectiveness on achieving semantic-sensitive video understanding. In this paper, we have developed a novel framework to achieve concept-oriented video skimming and it consists of three parts: (a) using salient objects for semantic-sensitive video content representation; (b) using finite mixture models for semantic video concept modeling and classification; (c) enabling concept-oriented video skimming via semantic video classification.

References

[1]
H. Luo, J. Fan, Y. Gao, G. Xu, "Multimodal salient objects: General building blocks of semantic video concepts", CIVR, 2004.
[2]
J. Fan, H. Luo, A.K. Elmagarmid, "Concept-oriented indexing of video database toward more effective retrieval and browsing", IEEE Trans. on Image Processing, vol.13, no.7, 2004.
[3]
H. Sundaram, L. Xie, S.-F. Chang, "A unility framework for the automatic generation of audio-visual skims", ACM Multimedia, 2002.

Cited By

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  • (2006)Large-scale video retrieval via semantic classificationProceedings of the 14th ACM international conference on Multimedia10.1145/1180639.1180834(884-886)Online publication date: 23-Oct-2006
  • (2006)A Progressive Framework for Two-Way Clustering Using Adaptive Subspace Iteration for Functionally Classifying GenesThe 2006 IEEE International Joint Conference on Neural Network Proceedings10.1109/IJCNN.2006.247254(2980-2985)Online publication date: 2006
  • (2006)Performance Evaluation of Subspace-based Algorithm in Selecting Differentially Expressed Genes and Classification of Tissue Types from Microarray DataThe 2006 IEEE International Joint Conference on Neural Network Proceedings10.1109/IJCNN.2006.247253(2972-2979)Online publication date: 2006

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  1. Concept-oriented video skimming via semantic video classification

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    cover image ACM Conferences
    MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia
    October 2004
    1028 pages
    ISBN:1581138938
    DOI:10.1145/1027527
    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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    New York, NY, United States

    Publication History

    Published: 10 October 2004

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    Author Tags

    1. concept-oriented video skimming
    2. semantic video classification

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

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    Cited By

    View all
    • (2006)Large-scale video retrieval via semantic classificationProceedings of the 14th ACM international conference on Multimedia10.1145/1180639.1180834(884-886)Online publication date: 23-Oct-2006
    • (2006)A Progressive Framework for Two-Way Clustering Using Adaptive Subspace Iteration for Functionally Classifying GenesThe 2006 IEEE International Joint Conference on Neural Network Proceedings10.1109/IJCNN.2006.247254(2980-2985)Online publication date: 2006
    • (2006)Performance Evaluation of Subspace-based Algorithm in Selecting Differentially Expressed Genes and Classification of Tissue Types from Microarray DataThe 2006 IEEE International Joint Conference on Neural Network Proceedings10.1109/IJCNN.2006.247253(2972-2979)Online publication date: 2006

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