Salient Object Detection via Fast Iterative Truncated Nuclear Norm Recovery

  • Chuhang Zou
  • Yao Hu
  • Deng Cai
  • Xiaofei He
Conference paper

DOI: 10.1007/978-3-642-42057-3_31

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8261)
Cite this paper as:
Zou C., Hu Y., Cai D., He X. (2013) Salient Object Detection via Fast Iterative Truncated Nuclear Norm Recovery. In: Sun C., Fang F., Zhou ZH., Yang W., Liu ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg

Abstract

Salient object detection is a challenging problem in many areas such as image segmentation and object recognition. Many approaches reveal that the background of an image usually lies in a low-dimensional subspace, while the salient regions perform as noises. Conventional methods apply nuclear norm minimization to recover the low-rank background to get the saliency. However, the nuclear norm could not approximate the rank operator properly. In this paper, we propose a novel salient object detection method called Fast Iterative Truncated Nuclear Norm Recovery (FIT) to detect salient objects. Recent proposed Truncated Nuclear Norm is used as a convex relaxation of the rank operator, which consequently guarantees a higher accuracy while reducing time consumption in saliency detection. Series of experiments have been conducted on widely used public database. The results demonstrate the efficiency of our proposed algorithm compared with the state-of-the-art.

Keywords

Salient Object Detection Low-rank Matrix Recovery Truncated Nuclear Norm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Chuhang Zou
    • 1
  • Yao Hu
    • 1
  • Deng Cai
    • 1
  • Xiaofei He
    • 1
  1. 1.Zhejiang UniversityHangZhouChina

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