基于稀疏化图结构的转导多标注视频概念检测算法

Sparse Graph Based Transductive Multi-Label Learning for Video Concept Detection

提出一种基于稀疏化图结构的转导多标注视频概念检测算法.首先,该方法通过信号稀疏化表达方法挖掘样本间视觉相似性关系与概念间分布相关性关系.然后,基于离散隐马尔可夫随机场构建多标注稀疏化图结构完成转导半监督视频概念检测.相关性信息的稀疏化表达可有效去除冗余信息的影响,降低图分类算法的问题复杂度,提高概念检测效率和分类效果.算法在TRECVID2005数据集上进行实验,并与多种有监督、半监督分类算法进行结果比较.实验结果验证该算法的有效性.

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.