Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Image Spaces and Video Trajectories: Using Isomap to Explore Video Sequences
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ClusterMap: labeling clusters in large datasets via visualization
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Video event segmentation and visualisation in non-linear subspace
Pattern Recognition Letters
Boosting discriminant learners for gait recognition using MPCA features
Journal on Image and Video Processing - Special issue on video-based modeling, analysis, and recognition of human motion
MPCA: Multilinear Principal Component Analysis of Tensor Objects
IEEE Transactions on Neural Networks
A survey of multilinear subspace learning for tensor data
Pattern Recognition
| Hi-index | 0.00 |
This paper presents a novel approach for the visualization and clustering of crowd video contents by using multilinear principal component analysis (MPCA). In contrast to feature-point-based approach and frame-based dimensionality reduction approach, the proposed method maps each short video segment to a point in MPCA subspace to take temporal information into account naturally through tensorial representations. Specifically, MPCA projects each short segment of a video to a low-dimensional tensor first. A few MPCA features are then selected according to the variance captured as the final representation. Thus, a video is visualized as a trajectory in MPCA subspace. The trajectory generated enables visual interpretation of video content in a compact space as well as visual clustering of video events. The proposed method is evaluated on the PETS 2009 datasets through comparison with three existing methods for video visualization. The MPCA visualization shows superior performance in clustering segments of the same event as well as identifying the transitions between events.