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Salient Region Detection by Jointly Modeling Distinctness and Redundancy of Image Content

  • Yiqun Hu
  • Zhixiang Ren
  • Deepu Rajan
  • Liang-Tien Chia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6493)

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.

Keywords

Gaussian Mixture Model Salient Object Salient Region Saliency Detection Visual Saliency 
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 2011

Authors and Affiliations

  • Yiqun Hu
    • 1
  • Zhixiang Ren
    • 1
  • Deepu Rajan
    • 1
  • Liang-Tien Chia
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore

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