The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Grassmann discriminant analysis: a unifying view on subspace-based learning
Proceedings of the 25th international conference on Machine learning
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear Mean Shift over Riemannian Manifolds
International Journal of Computer Vision
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
Kernel Grassmannian distances and discriminant analysis for face recognition from image sets
Pattern Recognition Letters
Human Action Recognition by Semilatent Topic Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
When Is There a Representer Theorem? Vector Versus Matrix Regularizers
The Journal of Machine Learning Research
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Action recognition using context and appearance distribution features
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Activity recognition using dynamic subspace angles
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
WACV '12 Proceedings of the 2012 IEEE Workshop on the Applications of Computer Vision
Using a Product Manifold distance for unsupervised action recognition
Image and Vision Computing
Advances in matrix manifolds for computer vision
Image and Vision Computing
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Isotonic CCA for sequence alignment and activity recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Hi-index | 0.10 |
Modelling video sequences by subspaces has recently shown promise for recognising human actions. Subspaces are able to accommodate the effects of various image variations and can capture the dynamic properties of actions. Subspaces form a non-Euclidean and curved Riemannian manifold known as a Grassmann manifold. Inference on manifold spaces usually is achieved by embedding the manifolds in higher dimensional Euclidean spaces. In this paper, we instead propose to embed the Grassmann manifolds into reproducing kernel Hilbert spaces and then tackle the problem of discriminant analysis on such manifolds. To achieve efficient machinery, we propose graph-based local discriminant analysis that utilises within-class and between-class similarity graphs to characterise intra-class compactness and inter-class separability, respectively. Experiments on KTH, UCF Sports, and Ballet datasets show that the proposed approach obtains marked improvements in discrimination accuracy in comparison to several state-of-the-art methods, such as the kernel version of affine hull image-set distance, tensor canonical correlation analysis, spatial-temporal words and hierarchy of discriminative space-time neighbourhood features.