A Semi-Supervised Method For Multimodal Classification of Consumer Videos

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.