Video Semantic Concept Detection based on Conceptual Correlation and Boosting
Semantic concept detection is a key technique to video semantic indexing. Traditional approaches did not take account of conceptual correlation adequately. A new approach based on conceptual correlation and boosting is proposed in this paper, including three steps: the context based conceptual fusion models using correlative concepts selection are built at first, then a boosting process based on inter-concept correlation is implemented, finally multi-models generated in boosting are fusioned. The experimental results on Trecvid 2005 dataset show that the proposed method achieves more remarkable and consistent improvement.
Video semantic concept detection Co-concept-boosting Context based conceptual fusion Conceptual correlation Inter-concept correlation
Danwen Chen Liqiong Deng Lingda Wu
Science and Technology on Information Systems Engineering Laboratory National University of Defense The Key Laboratory The Academy of Equipment Command & Technology Beijing, China
国内会议
北京
英文
1-4
2011-11-04（万方平台首次上网日期，不代表论文的发表时间）