International Journal of Computer Vision
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
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Blur Insensitive Texture Classification Using Local Phase Quantization
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Spatio-temporal pyramid matching for sports videos
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
Canonical Correlation Analysis of Video Volume Tensors for Action Categorization and Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Action Recognition Using LBP-TOP as Sparse Spatio-Temporal Feature Descriptor
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
DynTex: A comprehensive database of dynamic textures
Pattern Recognition Letters
WLD: A Robust Local Image Descriptor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum margin distance learning for dynamic texture recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
An evaluation of bags-of-words and spatio-temporal shapes for action recognition
WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
Volume local phase quantization for blur-insensitive dynamic texture classification
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Dynamic texture classification using dynamic fractal analysis
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Various visual tasks such as the recognition of human actions, gestures, facial expressions, and classification of dynamic textures require modeling and the representation of spatio-temporal information. In this paper, we propose representing space-time patterns using directional spatio-temporal oriented gradients. In the proposed approach, a 3D video patch is represented by a histogram of oriented gradients over nine symmetric spatio-temporal planes. Video comparison is achieved through a positive definite similarity kernel that is learnt by multiple kernel learning. A rich spatio-temporal descriptor with a simple trade-off between discriminatory power and invariance properties is thereby obtained. To evaluate the proposed approach, we consider three challenging visual recognition tasks, namely the classification of dynamic textures, human gestures and human actions. Our evaluations indicate that the proposed approach attains significant classification improvements in recognition accuracy in comparison to state-of-the-art methods such as LBP-TOP, 3D-SIFT, HOG3D, tensor canonical correlation analysis, and dynamical fractal analysis.