EXTRACTING MULTIMEDIA SEMANTICS BASED ON INDEPENDENT MODALITY DISCOVERING AND FUSION
Learning semantics from low-level features of multimedia learning resources enables high-level access to multimedia content. Considerable amount of researches have been focused on multi-modal analysis to detect multimedia semantics.However, two fundamental issues have not been adequately addressed. First, given a set of raw features extracted from multimedia sources, what are the best independent modalities?Second, once a set of modalities has been identified, how are they optimally fused to map to the high-level semantics? In this paper, we apply statistical and machine learning techniques to answer the two questions. ISOMAP combining with support vector clustering are used to discover independent modalities from raw features. Then Maximum Entropy method is applied to optimally fuse the individual modalities. Experiments show that the proposed method can learn multimedia semantics more efficiently than traditional methods.
Multimedia semantics ISOMAP Support vector clustering Multi-modal fusion
RUO-GUI XIAO YUE-TING ZHUANG FEI WU
Institute of Artificial Intelligence of Zhejiang University Zhejiang University, Hangzhou, China, 310027
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
2754-2758
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）