Modeling semantic concepts and user preferences in content-based video retrieval

In this paper, a user-centered framework is proposed for video database modeling and retrieval to provide appealing multimedia experiences on the content-based video queries. By incorporating the Hierarchical Markov Model Mediator (HMMM) mechanism, the source videos, segmented video shots, visual/audio features, semantic events, and high-level user perceptions are seamlessly integrated in a video database. With the hierarchical and stochastic design for video databases and semantic concept modeling, the proposed framework supports the retrieval for not only single events but also temporal sequences with multiple events. Additionally, an innovative method is proposed to capture the individual user's preferences by considering both the low-level features and the semantic concepts. The retrieval and ranking of video events and the temporal patterns can be updated dynamically online to satisfy individual user's interest and information requirements. Moreover, the users' feedbacks are efficiently accumulated for the offline system training process such that the overall retrieval performance can be enhanced periodically and continuously. For the evaluation of the proposed approach, a soccer video retrieval system is developed, presented, and tested to demonstrate the overall retrieval performance improvement achieved by modeling and capturing the user preferences.