Cancelable Biometric Filters for Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human motion recognition using Isomap and dynamic time warping
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Computer Vision and Image Understanding
Human activity modeling as brownian motion on shape manifold
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
| Hi-index | 0.00 |
High dimensional data sequences, such as video clips, can be modeled as trajectories in a high dimensional space and, and usually exhibit a low dimensional structure intrinsic to each distinct class of data sequence [1]. In this paper, we exploit a fibre bundle formalism to model various realizations of each trajectory, and characterize these high dimensional data sequences by an optimal operator subspace. Each operator is calculated as a matched filter corresponding to a standard Gaussian output with the data as input. The low dimensional structure intrinsic to the data is further explored, by minimizing the dimension of the operator space under data driven constraints. The dimension minimization problem is reformulated as a convex nuclear norm minimization problem, and an associated algorithm is proposed. Moreover, a fast method with superior performance for video based human activity classification is implemented by searching for an optimal operator space and adapted to the data. Illustrating examples demonstrating the performance of this approach are presented.