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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.
@inproceedings{1009136, author = {Jianping Fan and Hangzai Luo}, title = {Semantic video classification by integrating unlabeled samples for classifier training}, booktitle = {SIGIR '04: Proceedings of the 27th annual international conference on Research and development in information retrieval}, year = {2004}, isbn = {1-58113-881-4}, pages = {592--593}, location = {Sheffield, United Kingdom}, doi = {http://doi.acm.org/10.1145/1008992.1009136}, publisher = {ACM Press}, }  ©2005 Association for Computing Machinery |
