Salient region detection by jointly modeling distinctness and redundancy of image content

  • Authors:

  • Yiqun Hu;Zhixiang Ren;Deepu Rajan;Liang-Tien Chia

  • Affiliations:

  • School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore

  • Venue:
  • ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
  • Year:
  • 2010

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Abstract

Salient region detection in images is a challenging task, despite its usefulness in many applications. By modeling an image as a collection of clusters, we design a unified clustering framework for salient region detection in this paper. In contrast to existing methods, this framework not only models content distinctness from the intrinsic properties of clusters, but also models content redundancy from the removed content during the retargeting process. The cluster saliency is initialized from both distinctness and redundancy and then propagated among different clusters by applying a clustering assumption between clusters and their saliency. The novel saliency propagation improves the robustness to clustering parameters as well as retargeting errors. The power of the proposed method is carefully verified on a standard dataset of 5000 real images with rectangle annotations as well as a subset with accurate contour annotations.