Volume 5, Issue 1 (3-2013)                   2013, 5(1): 19-26 | Back to browse issues page

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Karimian M, Tavassolipour M, Kasaei S. A Semi-Supervised Method for Multimodal Classification of Consumer Videos . International Journal of Information and Communication Technology Research 2013; 5 (1) :19-26
URL: http://ijict.itrc.ac.ir/article-1-163-en.html
Abstract:   (2506 Views)

In large databases, lack of labeled training data leads to major difficulties in classification process. Semi-supervised algorithms are employed to suppress this problem. Video databases are the epitome for such a scenario. Fortunately, graph-based methods have shown to form promising platforms for semi-supervised video classification. Based on multimodal characteristics of video data, different features (SIFT, STIP, and MFCC) have been utilized to build the 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 that tries to find the correct label for unlabeled data during its iterations. But, unlike other co-training methods, it takes into account the unlabeled data in the classification process. After manifold regularization, data fusion is doneby a ranking method which improves the algorithm to become competitive with supervised methods. Our experimental results, run on the CCV database, show the efficiency of the proposed classification method.

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Type of Study: Research | Subject: Information Technology

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