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What Is the Chance of Happening: A New Way to Predict Where People Look

  • Yezhou Yang
  • Mingli Song
  • Na Li
  • Jiajun Bu
  • Chun Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)

Abstract

Visual attention is an important issue in image and video analysis and keeps being an open problem in the computer vision field. Motivated by the famous Helmholtz principle, a new approach of visual attention analysis is proposed in this paper based on the low level feature statistics of natural images and the Bayesian framework. Firstly, two priors, i.e., Surrounding Feature Prior (SFP) and Single Feature Probability Distribution (SFPD) are learned and integrated by a Bayesian framework to compute the chance of happening (CoH) of each pixel in an image. Then another prior, i.e., Center Bias Prior (CBP), is learned and applied to the CoH to compute the saliency map of the image. The experimental results demonstrate that the proposed approach is both effective and efficient by providing more accurate and quick visual attention location. We make three major contributions in this paper: (1) A set of simple but powerful priors, SFP, SFPD and CBP, are presented in an intuitive way; (2) A computational model of CoH based on Bayesian framework is given to integrate SFP and SFPD together; (3) A computationally plausible way to obtain the saliency map of natural images based on CoH and CBP.

Keywords

Visual Attention Human Visual System Natural Image Bayesian Framework Independent Component Analysis 
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 2010

Authors and Affiliations

  • Yezhou Yang
    • 1
  • Mingli Song
    • 1
  • Na Li
    • 1
  • Jiajun Bu
    • 1
  • Chun Chen
    • 1
  1. 1.Zhejiang UniversityHangzhouChina

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