Video Semantic Concept Detection Based on MultiModality Fusion
Multiple kernel learning methods have a widespread application in visual concept learning and BoVW method has been widely used dues to its excellent categorization performance. However, most canonical multiple kernel learning methods employ a stationary kernel combination format which assigns a uniform kernel weights over the input space. And BoVW method aimed to resolve the problem that the time efficiency of BoVW method decreases as the visual data scales up. As it is true for human perception, learning from multimodalities has become an effective scheme for various information retrieval problems. In this paper, we propose a novel multi-modality fusion approach for video search, where the search modalities are derived from a diverse set of knowledge sources. Our proposed approach, explores a large set of predefined semantic concepts for computing multi-modality fusion weights by a new method. Experimental results validate the effectiveness of our approach, which outperforms the existing multi-modality fusion methods.
component Visual Semantic Concept multi-modality clustering Inter-Class Correlation
Zhao Jianxun Wu BO
Zhongzhou University Zhengzhou, China, 450044
国际会议
杭州
英文
334-338
2012-03-23（万方平台首次上网日期，不代表论文的发表时间）