Abstract
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 domainspecific 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.
Original language | Undefined |
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Title of host publication | Proceedings of the 11th Asian Conference on Computer Vision, ACCV 2012 |
Place of Publication | London |
Publisher | Springer |
Pages | 162-176 |
Number of pages | 14 |
ISBN (Print) | 978-3-642-37443-2 |
DOIs | |
Publication status | Published - Nov 2012 |
Event | 11th Asian Conference on Computer Vision, ACCV 2012 - Daejeon, Korea, Republic of Duration: 5 Nov 2012 → 9 Nov 2012 Conference number: 11 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer Verlag |
Volume | 7725 |
Conference
Conference | 11th Asian Conference on Computer Vision, ACCV 2012 |
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Abbreviated title | ACCV |
Country | Korea, Republic of |
City | Daejeon |
Period | 5/11/12 → 9/11/12 |
Keywords
- HMI-MI: MULTIMODAL INTERACTIONS
- METIS-296219
- IR-84323
- EWI-22888