Date of Original Version
Abstract or Description
According to some current thinking, a very large number of semantic concepts could provide researcher a novel way to characterize video and be utilized for video retrieval and understanding. These semantic concepts do not isolate to each other and thus exploiting relationships between multiple semantic concepts in video could be a very useful source to enhance the concept detection performance. In this paper we present a discriminative learning framework called Multi-concept Discriminative Random Field (MDRF) for building probabilistic models on video semantic concept detections by incorporating related concepts as well as the observation. The proposed model exploits the power of discriminative graphical models to simultaneously capture the associations of concept with observed data and the interactions between related concepts. Compared with previous methods, this model can not only capture the co-occurrence between concepts but also incorporate the data observation in a unified framework. We also present an approximate parameter estimation algorithm and apply it to TRECVID 2005 data. Our experiments show promising results compared to the single concept learning approach for video semantic detection.