Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Confidence-based dynamic ensemble for image annotation and semantics discovery
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Concept-oriented indexing of video databases: toward semantic sensitive retrieval and browsing
IEEE Transactions on Image Processing
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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.