Semantic video classification with insufficient labeled samples

To support more effective video retrieval at semantic level, we introduce a novel framework to achieve semantic video classification. This novel framework includes: (a) A semantic-senstive video content representation framework via principal video shots to enhance the quality of features (i.e., the ability of the selected low-level multimodal perceptual features to discriminate among various semantic video concepts); (b) A semantic video concept interpretation framework via flexible mixture model to bridge the semantic gap between the semantic video concepts and the low-level multimodal perceptual features; (c) A novel concept learning technique to integrate unlabeled samples with labeled samples for more accurate classifier training. Experimental results on semantic medical video classification are also presented to evaluate the performance of the proposed framework.