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Semantic video classification by integrating unlabeled samples for classifier training

Published: 25 July 2004 Publication History

Abstract

Semantic video classification has become an active research topic to enable more effective video retrieval and knowledge discovery from large-scale video databases. However, most existing techniques for classifier training require a large number of hand-labeled samples to learn correctly. To address this problem, we have proposed a semi-supervised framework to achieve incremental classifier training by integrating a limited number of labeled samples with a large number of unlabeled samples. Specifically, this emi-supervised framework includes: (a) Modeling the semantic video concepts by using the finite mixture models to approximate the class distributions of the relevant salient objects; (b) Developing an adaptive EM algorithm to integrate the unlabeled samples to achieve parameter estimation and model selection simultaneously; The experimental results in a certain domain of medical videos are also provided.

References

[1]
B. Li, K. Goh, E. Chang, "Confidence-based dynamic ensamble for image annotation and semantic discovery", ACM SIGMM, 2003.
[2]
K. Nigam, A. McCallum, S. Thrun, T. Mitchell, "Text classification from labeled and unlabeled documents using EM", Machine Learning, vol.39, no.2, 2000.
[3]
J J. Fan, H. Luo, A.K. Elmagarmid, "Concept-Oriented indexing of video databases towards more efficient retrieval and browsing", IEEE Trans. on Image Processing, vol.13, no.6, 2004.
[4]
J. Fan, Y. Gao, H. Luo, G. Xu, "Automatic image annotation by using concept-sensitive salient objects for image content representation", SIGIR, 2004.

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  1. Semantic video classification by integrating unlabeled samples for classifier training

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    cover image ACM Conferences
    SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
    July 2004
    624 pages
    ISBN:1581138814
    DOI:10.1145/1008992
    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: 25 July 2004

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    Author Tags

    1. adaptive EM algorithm
    2. finite mixture models
    3. semantic video classification
    4. unlabeled samples

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