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Incremental Slow Feature Analysis with Indefinite Kernel for Online Temporal Video Segmentation

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 7725)

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 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.

Keywords

  • Singular Value Decomposition
  • Scatter Matrix
  • Kernel Principal Component Analysis
  • Eigenvalue Decomposition
  • Krein Space

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Liwicki, S., Zafeiriou, S., Pantic, M. (2013). Incremental Slow Feature Analysis with Indefinite Kernel for Online Temporal Video Segmentation. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7725. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37444-9_13

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  • DOI: https://doi.org/10.1007/978-3-642-37444-9_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37443-2

  • Online ISBN: 978-3-642-37444-9

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