A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatiograms versus Histograms for Region-Based Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Histogram of oriented rectangles: A new pose descriptor for human action recognition
Image and Vision Computing
A survey on vision-based human action recognition
Image and Vision Computing
Recognizing Human Actions Using Key Poses
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Assessing Water Quality by Video Monitoring Fish Swimming Behavior
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
View-Independent Action Recognition from Temporal Self-Similarities
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The automatic analysis of video sequences with individuals performing some actions is currently receiving much attention in the computer vision community. Among the different visual features chosen to tackle the problem of action recognition, local histogram within a region of interest is proven to be very effective. However, we study for the first time whether spatiograms, which are histograms enriched with per-bin spatial information, can be alternatively effective for action characterization. On the other hand, the temporal information of these histograms is usually collapsed by simple averaging of the histograms, which basically ignores the dynamics of the action. In contrast, this paper explores a temporally holistic representation in the form of recurrence matrices which capture pair-wise spatiograms relationships on a frame-by-frame basis. Experimental results show that recurrence matrices are powerful for action classification, whereas spatiograms, in its current usage, are not.