Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Scale Invariant Action Recognition Using Compound Features Mined from Dense Spatio-temporal Corners
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Finding Actions Using Shape Flows
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Cross-View Action Recognition from Temporal Self-similarities
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Spatial-Temporal correlatons for unsupervised action classification
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Weakly supervised learning of part-based spatial models for visual object recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Sparse flexible models of local features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Selective spatio-temporal interest points
Computer Vision and Image Understanding
Using SAX representation for human action recognition
Journal of Visual Communication and Image Representation
Trajectory-Based modeling of human actions with motion reference points
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Exploiting sparse representations for robust analysis of noisy complex video scenes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Cost-Sensitive top-down/bottom-up inference for multiscale activity recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Human gesture recognition system for TV viewing using time-of-flight camera
Multimedia Tools and Applications
Human action recognition with salient trajectories
Signal Processing
Common-sense reasoning for human action recognition
Pattern Recognition Letters
Language-motivated approaches to action recognition
The Journal of Machine Learning Research
Silhouette-based human action recognition using SAX-Shapes
The Visual Computer: International Journal of Computer Graphics
Activity representation with motion hierarchies
International Journal of Computer Vision
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The popular bag-of-words paradigm for action recognition tasks is based on building histograms of quantized features, typically at the cost of discarding all information about relationships between them. However, although the beneficial nature of including these relationships seems obvious, in practice finding good representations for feature relationships in video is difficult. We propose a simple and computationally efficient method for expressing pairwise relationships between quantized features that combines the power of discriminative representations with key aspects of Naïve Bayes. We demonstrate how our technique can augment both appearance- and motion-based features, and that it significantly improves performance on both types of features.