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Pattern Mining Saliency

  • Yuqiu Kong
  • Lijun Wang
  • Xiuping Liu
  • Huchuan LuEmail author
  • Xiang Ruan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9910)

Abstract

This paper presents a new method to promote the performance of existing saliency detection algorithms. Prior bottom-up methods predict saliency maps by combining heuristic saliency cues, which may be unreliable. To remove error outputs and preserve accurate predictions, we develop a pattern mining based saliency seeds selection method. Given initial saliency maps, our method can effectively recognize discriminative and representative saliency patterns (features), which are robust to the noise in initial maps and can more accurately distinguish foreground from background. According to the mined saliency patterns, more reliable saliency seeds can be acquired. To further propagate the saliency labels of saliency seeds to other image regions, an Extended Random Walk (ERW) algorithm is proposed. Compared with prior methods, the proposed ERW regularized by a quadratic Laplacian term ensures the diffusion of seeds information to more distant areas and allows the incorporation of external classifiers. The contributions of our method are complementary to existing methods. Extensive evaluations on four data sets show that our method can significantly improve accuracy of existing methods and achieves more superior performance than state-of-the-arts.

Keywords

Saliency detection Pattern mining Random walk 

Notes

Acknowledgement

The work is supported by the National Natural Science Foundation of China under Grant 61370143, 61262050, 61528101 and 61472060.

Supplementary material

419981_1_En_35_MOESM1_ESM.pdf (29.2 mb)
Supplementary material 1 (pdf 29895 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Yuqiu Kong
    • 1
  • Lijun Wang
    • 2
  • Xiuping Liu
    • 1
  • Huchuan Lu
    • 2
    Email author
  • Xiang Ruan
    • 3
  1. 1.Department of Mathematical SciencesDalian University of TechnologyDalianChina
  2. 2.Department of Electrical EngineeringDalian University of TechnologyDalianChina
  3. 3.Tiwaki CorporationTiwakiJapan

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