Semantic Video Classification with Insucient Labeled Samples (original) (raw)

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Abstract

To support more eective video retrieval at semantic level, we introduce a novel framework to achieve seman- tic 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

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