Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
The Journal of Machine Learning Research
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
View Invariance for Human Action Recognition
International Journal of Computer Vision
Higher order learning with graphs
ICML '06 Proceedings of the 23rd international conference on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum unfolded embedding: formulation, solution, and application for image clustering
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Hypergraph spectral learning for multi-label classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Tracking and recognizing actions of multiple hockey players using the boosted particle filter
Image and Vision Computing
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Action Recognition by Semilatent Topic Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Randomized locality sensitive vocabularies for bag-of-features model
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Temporal feature weighting for prototype-based action recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Proximal Methods for Hierarchical Sparse Coding
The Journal of Machine Learning Research
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Regularization on discrete spaces
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Learning semantic features for action recognition via diffusion maps
Computer Vision and Image Understanding
Action recognition using context and appearance distribution features
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Robust classification using structured sparse representation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Spectral learning of latent semantics for action recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
| Hi-index | 0.01 |
This paper proposes a novel latent semantic learning method for extracting high-level latent semantics from a large vocabulary of abundant mid-level features (i.e. visual keywords) with structured sparse representation, which can help to bridge the semantic gap in the challenging task of human action recognition. To discover the manifold structure of mid-level features, we develop a graph-based spectral embedding approach to latent semantic learning, with the graph over mid-level features being constructed using sparse representation. Moreover, we define new L"1-norm hypergraph regularization to induce extra structured sparsity into sparse representation for graph construction. Due to the nice properties (sparsity and noise-robustness) of such structured sparse representation, our graph construction can capture dominant and robust relationships among mid-level features, which are crucial for the success of latent semantic learning in action recognition. Unlike the traditional latent semantic analysis based on topic models, our latent semantic learning method can explore the manifold structure of mid-level features in both graph construction and spectral embedding, which results in compact but discriminative high-level features. The experimental results on the commonly used KTH action dataset and unconstrained YouTube action dataset show the promising performance of our method.