Histogram clustering for unsupervised segmentation and image retrieval
Pattern Recognition Letters
Introduction to Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Parsing: Unifying Segmentation, Detection, and Recognition
International Journal of Computer Vision
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Context-Based Segmentation of Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Level Grouping for Video Shots
International Journal of Computer Vision
Object Recognition as Many-to-Many Feature Matching
International Journal of Computer Vision
Unsupervised Bayesian Detection of Independent Motion in Crowds
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Learning compositional categorization models
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Object categorization by compositional graphical models
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Tracking by Hierarchical Representation of Target Structure
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera
International Journal of Computer Vision
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The complexity of visual representations is substantially limited by the compositional nature of our visual world which, therefore, renders learning structured object models feasible. During recognition, such structured models might however be disadvantageous, especially under the high computational demands of video. This contribution presents a compositional approach to video analysis that demonstrates the value of compositionality for both, learning of structured object models and recognition in near real-time. We unite category-level, multi-class object recognition, segmentation, and tracking in the same probabilistic graphical model. A model selection strategy is pursued to facilitate recognition and tracking of multiple objects that appear simultaneously in a video. Object models are learned from videos with heavy clutter and camera motion where only an overall category label for a training video is provided, but no hand-segmentation or localization of objects is required. For evaluation purposes a video categorization database is assembled and experiments convincingly demonstrate the suitability of the approach.