On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Event Detection and Analysis from Video Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
The Journal of Machine Learning Research
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Detecting Irregularities in Images and in Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Visual tracking and recognition using probabilistic appearance manifolds
Computer Vision and Image Understanding
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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We address the recognition problem of video activities involving two interacting moving objects under a surveillance camera. We develop a novel video activity representation scheme --'bag of segments'. In this scheme, the video sessions are represented as a collection of independent segments, with memberships to each pre-learned visual patterns that we call codewords. To better represent the video segments with object interaction, we design a set of new features based on the prediction filter responses and the Granger Causality Test (GCT). These features capture the inter-relationship between moving objects and are combined with conventional features such as position and velocity. We validate the proposed method for the task of video activities classification with extensive experiments on a surveillance database with 867 video sessions.