A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Unsupervised view and rate invariant clustering of video sequences
Computer Vision and Image Understanding
Statistical Methods and Models for Video-Based Tracking, Modeling, and Recognition
Foundations and Trends in Signal Processing
A survey on vision-based human action recognition
Image and Vision Computing
Combining models of pose and dynamics for human motion recognition
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Action categorization by structural probabilistic latent semantic analysis
Computer Vision and Image Understanding
Novel kernel-based recognizers of human actions
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
Unsupervised action classification using space-time link analysis
Journal on Image and Video Processing
Boosted multi-class semi-supervised learning for human action recognition
Pattern Recognition
Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models
International Journal of Computer Vision
Human action recognition using pyramid vocabulary tree
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
Unsupervised temporal commonality discovery
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
On recognizing actions in still images via multiple features
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Exploiting language models to recognize unseen actions
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Discriminative hierarchical part-based models for human parsing and action recognition
The Journal of Machine Learning Research
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In this paper we consider the problem of describing the action being performed by human figures in still images. We will attack this problem using an unsupervised learning approach, attempting to discover the set of action classes present in a large collection of training images. These action classes will then be used to label test images. Our approach uses the coarse shape of the human figures to match pairs of images. The distance between a pair of images is computed using a linear programming relaxation technique. This is a computationally expensive process, and we employ a fast pruning method to enable its use on a large collection of images. Spectral clustering is then performed using the resulting distances. We present clustering and image labeling results on a variety of datasets.