Optimizing multi-graph learning

Learning based semantic video annotation is a promising approach for enabling content-based video search. However, severe difficulties, such as insufficiency of training data and curse of dimensionality, are frequently encountered. This paper proposes a novel unified scheme, Optimized Multi-Graph-based Semi-Supervised Learning (OMG-SSL), to simultaneously attack these difficulties. Instead of only using a single graph, OMG-SSL integrates multiple graphs into a regularization and optimization framework to sufficiently explore their complementary nature. We then show that various crucial factors in video annotation, including multiple modalities, multiple distance metrics, and temporal consistency, in fact all correspond to different correlations among samples, and hence they can be represented by different graphs. Therefore, OMG-SSL is able to simultaneously deal with these factors within a unified framework. Experiments on the TRECVID benchmark demonstrate the effectiveness of our proposed approach.