A Semi-Supervised Method for Multimodal Classification of Consumer Videos
Publish place: International Journal of Information and Communication Technology Research (IJICT، Vol: 5، Issue: 1
Publish Year: 1391
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_ITRC-5-1_003
تاریخ نمایه سازی: 22 فروردین 1401
Abstract:
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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