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《Journal of Data Acquisition and Processing》 2011-05
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Video Semantic Concept Detection Based on Evidence Theory

Wan Jianping,Peng Tianqiang,Li Bicheng(Institute of Information Engineering,Information Engineering University of PLA,Zhengzhou,450002,China)  
Video semantic concept detection is a prerequisite to solve the 'emantic gap' problem and realize semantic-based video retrieval.A video semantic concept detecting method based on the evidence theory is proposed.Firstly,features including grid color moment,wavelet texture and edge direction histogram are extracted from the key frames of video shots.Then,for each type of feature,an SVM model is trained.Thirdly,by analyzing the generalization error of each SVM model,a discounting coefficient method is implemented to modify the classification results of these models.Finally,these modified results are fused with an evidence fusion equation,and the fused result is regarded as the final semantic concept detection result.Experimental results show that the new method has improved the detection accuracy and outperforms the traditional linear classifier fusion method.
【Fund】: 国家自然科学基金(60872142)资助项目
【CateGory Index】: TP391.41
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