An Introduction to Variational Methods for Graphical Models
Machine Learning
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Principled Approach to Detecting Surprising Events in Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
International Journal of Computer Vision
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Nonchronological Video Synopsis and Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Anticipatory Behavior in Adaptive Learning Systems
COMPSAC '09 Proceedings of the 2009 33rd Annual IEEE International Computer Software and Applications Conference - Volume 02
Bayesian surprise and landmark detection
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Local descriptors for spatio-temporal recognition
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
Self-help: Seeking out perplexing images for ever improving topological mapping
International Journal of Robotics Research
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Automatic processing of video data is essential in order to allow efficient access to large amounts of video content, a crucial point in such applications as video mining and surveillance. In this paper we focus on the problem of identifying interesting parts of the video. Specifically, we seek to identify atypical video events, which are the events a human user is usually looking for. To this end we employ the notion of Bayesian surprise, as defined in [1, 2], in which an event is considered surprising if its occurrence leads to a large change in the probability of the world model. We propose to compute this abstract measure of surprise by first modeling a corpus of video events using the Latent Dirichlet Allocation model. Subsequently, we measure the change in the Dirichlet prior of the LDA model as a result of each video event's occurrence. This change of the Dirichlet prior leads to a closed form expression for an event's level of surprise, which can then be inferred directly from the observed data. We tested our algorithm on a real dataset of video data, taken by a camera observing an urban street intersection. The results demonstrate our ability to detect atypical events, such as a car making a U-turn or a person crossing an intersection diagonally.