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- W1501643721 abstract "This paper presents a hybrid approach to the classification of news video story. Most of current works on news story classification utilize the multimodal features in a uniform manner. However, the reliability of audio-visual confidence is much lower than that of text, which may evidently lower-down the performance of the classification. We proposed a decision strategy mainly depends on the evidence from text classifiers with extra assistance of audio-visual clues. In our approach, SVMs for text features and GMMs for audio-visual features are first built for each category and then used to compute text and audio-visual confidence vectors respectively. To make final decision, a text-biased decision strategy is proposed to combine these multimodal confidence vectors. To validate the performance, text-based classification and SVM-based meta-classification methods are compared on large-scale news stories from TV programs, and our proposed hybrid approach achieves the best overall performance." @default.
- W1501643721 created "2016-06-24" @default.
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- W1501643721 date "2004-07-08" @default.
- W1501643721 modified "2023-10-16" @default.
- W1501643721 title "A hybrid approach to news video classification with multi-modal features" @default.
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- W1501643721 doi "https://doi.org/10.1109/icics.2003.1292564" @default.
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