Graph-based multi-space semantic correlation propagation for video retrieval

By introducing the concept detection results to the retrieval process, concept-based video retrieval (CBVR) has been successfully used for semantic content-based video retrieval application. However, how to select and fuse the appropriate concepts for a specific query is still an important but difficult issue. In this paper, we propose a novel and effective concept selection method, named graph-based multi-space semantic correlation propagation (GMSSCP), to explore the relationship between the user query and concepts for video retrieval application. Compared with traditional methods, GMSSCP makes use of a manifold-ranking algorithm to collectively explore the multi-layered relationships between the query and concepts, and the expansion result is more robust to noises. Parallel to this, GMSSCP has a query-adapting property, which can enhance the process of concept correlation propagation and selection with strong pertinence of query cues. Furthermore, it can dynamically update the unified propagation graph by flexibly introducing the multi-modal query cues as additional nodes, and is not only effective for automatic retrieval but also appropriate for the interactive case. Encouraging experimental results on TRECVID datasets demonstrate the effectiveness of GMSSCP over the state-of-the-art concept selection methods. Moreover, we also apply it to the interactive retrieval system—VideoMap and gain an excellent performance and user experience.