Slow feature analysis: unsupervised learning of invariances
Neural Computation
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Unsupervised view and rate invariant clustering of video sequences
Computer Vision and Image Understanding
Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust FFT-Based Scale-Invariant Image Registration with Image Gradients
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regularized sparse Kernel slow feature analysis
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Slow Feature Analysis for Human Action Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Joint segmentation and classification of human actions in video
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Incremental slow feature analysis
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Sequential Karhunen-Loeve basis extraction and its application to images
IEEE Transactions on Image Processing
Incremental Kernel Principal Component Analysis
IEEE Transactions on Image Processing
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Slow Feature Analysis (SFA) is a subspace learning method inspired by the human visual system, however, it is seldom seen in computer vision. Motivated by its application for unsupervised activity analysis, we develop SFA's first implementation of online temporal video segmentation to detect episodes of motion changes. We utilize a domain-specific indefinite kernel which takes the data representation into account to introduce robustness. As our kernel is indefinite (i.e. defines instead of a Hilbert, a Krein space), we formulate SFA in Krein space. We propose an incremental kernel SFA framework which utilizes the special properties of our kernel. Finally, we employ our framework to online temporal video segmentation and perform qualitative and quantitative evaluation.