Recognizing Human Actions in Videos Acquired by Uncalibrated Moving Cameras
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex multi-task feature learning
Machine Learning
Learning to Recognize Activities from the Wrong View Point
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
IEEE Transactions on Knowledge and Data Engineering
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
View and style-independent action manifolds for human activity recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
View-Independent Action Recognition from Temporal Self-Similarities
IEEE Transactions on Pattern Analysis and Machine Intelligence
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Action recognition using context and appearance distribution features
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Cross-view action recognition via view knowledge transfer
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Discriminative virtual views for cross-view action recognition
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Multiclass transfer learning from unconstrained priors
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Domain adaptation for object recognition: An unsupervised approach
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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A novel transfer learning approach, referred to as Transfer Discriminant-Analysis of Canonical Correlations (Transfer DCC), is proposed to recognize human actions from one view (target view) via the discriminative model learned from another view (source view). To cope with the considerable change between feature distributions of source view and target view, Transfer DCC includes an effective nonparametric criterion in the discriminative function to minimize the mismatch between data distributions of these two views. We utilize the canonical correlation between the means of samples from source view and target view to measure the data distribution distance between the two views. Consequently, Transfer DCC learns an optimal projection matrix by simultaneously maximizing the canonical correlation of mean samples from source view and target view, maximizing the canonical correlations of within-class samples and minimizing the canonical correlations of between-class samples. Moreover, we propose a Weighted Canonical Correlations scheme to fuse the multi-class canonical correlations from multiple source views according to their corresponding weights for recognition in the target view. Experiments on the IXMAS multi-view dataset demonstrate the effectiveness of our method.