数据采集与处理2011，Vol.26Issue(5)：536-541,6.
基于证据理论的视频语义概念检测
Video Semantic Concept Detection Based on Evidence Theory
摘要
Abstract
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.关键词
视频语义概念/支持向量机/证据理论/分类器融合Key words
video semantic concept/support vector machine/evidence theory/classifier fusion分类
信息技术与安全科学引用本文复制引用
万建平,彭天强,李弼程..基于证据理论的视频语义概念检测[J].数据采集与处理,2011,26(5):536-541,6.基金项目
国家自然科学基金(60872142)资助项目. （60872142）
