Sparse Graph Based Transductive Multi-Label Learning for Video Concept Detection
ZHAO Ying-Hai1,2, CAI Jun-Jie1, WU Xiu-Qing1, SUN Fu-Ming3
1.School of Information Science and Technology, University of Science and Technology of China, Hefei 230027 2.The 35th Research Institute of China Aerospace Science and Industry Corp., Beijing 100013 3.College of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001
Abstract A sparse graph based transductive multi-label learning method is proposed for video concept detection. Firstly, the sparse signal representation theory is exploited to mine the point-wise similarity relationships and the concept-wise distribution correlation relationships. Then, the multi-label sparse graph structure is constructed based on discrete hidden Markov random field to conduct transductive semi-supervised video concept detection. The sparse representation for correlative information can remove the negative effect of redundant information, reduce the complexity of graph-based classification problem and improve the model efficiency and discriminability. The proposed method is evaluated on the TRECVID 2005 dataset, and extensive comparative experiments are conducted with respect to multiple supervised and semi-supervised classification methods. The experimental results demonstrate the effectiveness of the proposed method.
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2011, Vol. 24 
