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Exploiting multiview properties in semi-supervised video classification

Karimian, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/ISTEL.2012.6483102
  3. Abstract:
  4. In large databases, availability of labeled training data is mostly prohibitive in classification. Semi-supervised algorithms are employed to tackle the lack of labeled training data problem. Video databases are the epitome for such a scenario; that is why semi-supervised learning has found its niche in it. Graph-based methods are a promising platform for semi-supervised video classification. Based on the multiview characteristic of video data, different features have been proposed (such as SIFT, STIP and MFCC) which can be utilized to build a graph. In this paper, we have proposed a new classification method which fuses the results of manifold regularization over different graphs. Our method acts like a co-training method with respect to its iterative nature which tries to find the labels of unlabeled data during each iteration, but unlike co-training methods it takes into account the unlabeled data in classification procedure. The fusion is done after manifold regularization with a ranking method which makes the algorithm to be competitive with supervised methods. Our experimental results run on the CCV database show the efficiency of the proposed method
  5. Keywords:
  6. Manifold regularization ; Multiview features ; Semi-supervised learning ; Video classification ; Co-training ; Manifold regularizations ; Multi-views ; Classification (of information) ; Image classification ; Natural language processing systems ; Supervised learning ; Video recording ; Iterative methods
  7. Source: 2012 6th International Symposium on Telecommunications, IST 2012 ; 2012 , Pages 837-842 ; 9781467320733 (ISBN)
  8. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6483102