Sparse graph based transductive multi-label learning for video concept detection

作者：Zhao Ying Hai^*; Cai Jun Jie; Wu Xiu Qing; Sun Fu Ming

来源：Pattem Recognition and Aitificial Intelligence, 2011, 24(6): 825-832.

• Comparative experiments
• Concept detection
• Data sets
• Discriminability
• Graph-based
• Hidden Markov random fields
• Learning methods
• Model efficiency
• Multi-label
• Redundant informations
• Semi-supervised
• Semi-supervised classification method
• Semi-supervised learning
• Sparse graphs
• Sparse representation
• Sparse signal representation
• TRECVID
• Video Concept Detection

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.

• 出版日期2011
• 单位中国科学技术大学