A Semi-supervised Method for Multimodal Classification of Consumer Videos

In large databases, the lack of labeled training data leads to major difficulties in classification. 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 cotraining method that tries to find the correct label of unlabeled data during its iterations; But, unlike other co-training methods, it takes into account the unlabeled data in the classification process. The fusion is done after manifold regularization with a ranking method which makes the algorithm to become competitive with supervised methods. Our experimental results, run on the CCV database, show the efficiency of the proposed method. Keywords-Semi-supervised learning; co-training; video classification; multimodal features.