Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Modeling in Image Analysis
Markov Random Field Modeling in Image Analysis
TRECVID: benchmarking the effectiveness of information retrieval tasks on digital video
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Graph-based semi-supervised learning with multiple labels
Journal of Visual Communication and Image Representation
Event detection in sports video based on generative-discriminative models
EiMM '09 Proceedings of the 1st ACM international workshop on Events in multimedia
Structured max-margin learning for multi-label image annotation
Proceedings of the ACM International Conference on Image and Video Retrieval
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A current trend in video analysis research hypothesizes that a very large number of semantic concepts could provide a novel way to characterize, retrieve and understand video. These semantic concepts do not appear in isolatation to each other and thus it could be very useful to exploit the relationships between multiple semantic concepts to enhance the concept detection performance in video. In this paper we present a discriminative learning framework called Multi-concept Discriminative Random Field (MDRF) for building probabilistic models of video semantic concept detectors by incorporating related concepts as well as the low-level observations. 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 not only captures the co-occurrence between concepts but also incorporates the raw data observations into a unified framework. We also present an approximate parameter estimation algorithm and apply it to the TRECVID 2005 data. Our experiments show promising results compared to the single concept learning approach for semantic concept detection in video.