Non-local spatial redundancy reduction for bottom-up saliency estimation

  • Authors:

  • Jinjian Wu;Fei Qi;Guangming Shi;Yongheng Lu

  • Affiliations:

  • School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710071, PR China;School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710071, PR China;School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710071, PR China;School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710071, PR China

  • Venue:
  • Journal of Visual Communication and Image Representation
  • Year:
  • 2012

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Abstract

In this paper we present a redundancy reduction based approach for computational bottom-up visual saliency estimation. In contrast to conventional methods, our approach determines the saliency by filtering out redundant contents instead of measuring their significance. To analyze the redundancy of self-repeating spatial structures, we propose a non-local self-similarity based procedure. The result redundancy coefficient is used to compensate the Shannon entropy, which is based on statistics of pixel intensities, to generate the bottom-up saliency map of the visual input. Experimental results on three publicly available databases demonstrate that the proposed model is highly consistent with the subjective visual attention.