Online Kernel Slow Feature Analysis for Temporal Video Segmentation and Tracking

Slow feature analysis (SFA) is a dimensionality reduction technique which has been linked to how visual brain cells work. In recent years, the SFA was adopted for computer vision tasks. In this paper, we propose an exact kernel SFA (KSFA) framework for positive definite and indefinite kernels in Kre...

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Bibliographic Details
Published in:IEEE transactions on image processing 2015-10, Vol.24 (10), p.2955-2970
Main Authors: Liwicki, Stephan, Zafeiriou, Stefanos P., Pantic, Maja
Format: Article
Language:English
Subjects:
Algorithms
Biomimetics - methods
Change detection
Data transmission
Eigenvalues and eigenfunctions
Entrepreneurs
Feature extraction
Hilbert space
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Image segmentation
Kernel
Kernels
Matrix decomposition
Neural Networks (Computer)
Online
online kernel learning
Pattern Recognition, Automated - methods
Photography - methods
Reproducibility of Results
Segmentation
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Slow feature analysis
Streaming media
Subtraction Technique
temporal segmentation
Tracking
Video Recording - methods
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Summary:Slow feature analysis (SFA) is a dimensionality reduction technique which has been linked to how visual brain cells work. In recent years, the SFA was adopted for computer vision tasks. In this paper, we propose an exact kernel SFA (KSFA) framework for positive definite and indefinite kernels in Krein space. We then formulate an online KSFA which employs a reduced set expansion. Finally, by utilizing a special kind of kernel family, we formulate exact online KSFA for which no reduced set is required. We apply the proposed system to develop a SFA-based change detection algorithm for stream data. This framework is employed for temporal video segmentation and tracking. We test our setup on synthetic and real data streams. When combined with an online learning tracking system, the proposed change detection approach improves upon tracking setups that do not utilize change detection.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2015.2428052