Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Saliency, Scale and Image Description
International Journal of Computer Vision
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Action at a Distance
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
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Efficient Visual Event Detection Using Volumetric Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Handbook of Mathematical Models in Computer Vision
Handbook of Mathematical Models in Computer Vision
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
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
Spatiotemporal salient points for visual recognition of human actions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, we propose a novel Spatiotemporal Interest Point (MC-STIP) detector based on the coherent motion pattern around each voxel in videos. Our detector defines the local peaks of optical flow as the interest points in the motion coherence volumes of videos. A concatenating histogram of 2D gradients is introduced to describe each interest point as the descriptor. Moreover, we introduce a Topic Matrix Video Representation (T-Mat) for videos. Our representation not only captures the global hidden topics but also preserves the shared discriminative information among the interest point descriptors. We conduct our experiments on three benchmark datasets to recognize human actions using Support Vector Machines with four different kernels. The experiments demonstrate the effectiveness of our new approach.