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Saliency Detection Using Joint Temporal and Spatial Decorrelation

  • Hamed Rezazadegan Tavakoli
  • Esa Rahtu
  • Janne Heikkilä
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)

Abstract

This article presents a scene-driven (i.e. bottom-up) visual saliency detection technique for videos. The proposed method utilizes non-negative matrix factorization (NMF) to replicate neural responses of primary visual cortex neurons in spatial domain. In temporal domain, principal component analysis (PCA) was applied to imitate the effect of stimulus change experience during neural adaptation phenomena. We apply the proposed saliency model to background subtraction problem. The proposed method does not rely on any background model and is purely unsupervised. In experimental results, it will be shown that the proposed method competes well with some of the state-of-the-art background subtraction techniques especially in dynamic scenes.

Keywords

Independent Component Analysis Sparse Code Salient Region Equal Error Rate Saliency Detection 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hamed Rezazadegan Tavakoli
    • 1
  • Esa Rahtu
    • 1
  • Janne Heikkilä
    • 1
  1. 1.Center for Machine Vision ResearchUniversity of OuluFinland

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