Merging and Splitting Eigenspace Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
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
Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Spatial-Temporal correlatons for unsupervised action classification
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Semi-latent Dirichlet allocation: a hierarchical model for human action recognition
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Human action recognition based on graph-embedded spatio-temporal subspace
Pattern Recognition
Kernel sparse locality preserving canonical correlation analysis for multi-modal feature extraction
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
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Human action recognition from video sequences is a challenging problem due to the large changes of human appearance in the cases of partial occlusions, non-rigid deformations, and high irregularities. It is difficult to collect a large set of training samples to learn the discriminative model with covering all possible variations of an action. In this paper, we propose an online recognition method, namely incremental discriminant-analysis of canonical correlations (IDCC), in which the discriminative model is incrementally updated to capture the changes of human appearance, and thereby facilitates the recognition task in changing environments. As the training sets are acquired sequentially instead of being given completely in advance, our method is able to compute a new discriminant matrix by updating the existing one using the eigenspace merging algorithm. Furthermore, we integrate our method into the graph-based semi-supervised learning method, linear neighbor propagation, to deal with the limited labeled training data. Experimental results on both Weizmann and KTH action data sets show that our method performs better than state-of-the-art methods on accuracy and efficiency.