基于概念选择和重要性度量的多模态语义融合

根据人类认知过程的特性，提出语义选择和重要性度量的多模态融合算法. 分别在单个模态下获取语义概念，并利用相关性检测得到用于融合的语义概念，从而减少误检语义带来的扩散误差. 考虑到概率融合无法体现语义的时间特性，提出重要性度量的概念进行融合以获取高级语义. 实验结果表明，该方法能准确提取视频的高级语义信息，与其他融合算法相比时体现出良好的性能.

Multimodal Fusion Based on Concept Selection and Importance Measure

According to the characteristics of human cognitive processes, a multimodal fusion method of semantic concept selection and importance measure is proposed. Semantic concepts are first obtained in single mode, and a correlation detection method is used to decide semantic concept of fusion. Correlation detection can reduce the influence of diffusion error due to false detection semantic. Since the method of probability fusion cannot effectively handle the temporal characteristics of the semantic, an importance measure is introduced for high level semantic fusion. Experimental results show that the proposed method using temporal characteristics and relativity between concepts can better extract high-level semantic contents as compared to other fusion methods.