Imperial College London

STEFANOS ZAFEIRIOU, PhD

Faculty of EngineeringDepartment of Computing

Professor in Machine Learning & Computer Vision
 
 
 
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Contact

 

+44 (0)20 7594 8461s.zafeiriou Website CV

 
 
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Location

 

375Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Liwicki:2013:10.1007/978-3-642-37444-9_13,
author = {Liwicki, S and Zafeiriou, S and Pantic, M},
doi = {10.1007/978-3-642-37444-9_13},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {162--176},
title = {Incremental slow feature analysis with indefinite kernel for online temporal video segmentation},
url = {http://dx.doi.org/10.1007/978-3-642-37444-9_13},
volume = {7725 LNCS},
year = {2013}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - 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. © 2013 Springer-Verlag.
AU - Liwicki,S
AU - Zafeiriou,S
AU - Pantic,M
DO - 10.1007/978-3-642-37444-9_13
EP - 176
PY - 2013///
SN - 0302-9743
SP - 162
TI - Incremental slow feature analysis with indefinite kernel for online temporal video segmentation
T2 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
UR - http://dx.doi.org/10.1007/978-3-642-37444-9_13
VL - 7725 LNCS
ER -