Saliency Detection Based on Manifold Ranking and Refined Seed Labels

  • Shan Su
  • Ziguan CuiEmail author
  • Yutao Yao
  • Zongliang Gan
  • Guijin Tang
  • Feng Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11901)


Graph-based manifold ranking has been exploited for saliency detection with seed labels. However, when the selected labels are not accurate, these methods can’t emphasize the foreground and suppress the background effectively. In this paper, we propose a novel saliency detection approach through manifold ranking and refined seed labels. We first construct a half-two layers graph based on the nodes after superpixel segmentation, which is generated by connecting each node to neighboring nodes and the half of the most similar nodes that share common boundaries with neighboring nodes. Then we compute superpixel saliency using manifold ranking with refined labels by two-step manner. After clustering superpixel with K-means, the background-based detection is obtained by refined background labels, which are those clusters containing boundary. The foreground-based detection is acquired with the refined foreground labels which are the complete cluster after thresholding the background-based detection. The proposed method has been tested on four universal datasets: ASD, CSSD, ECSSD and SOD. Experimental results show that our method performs better than prior similar state-of-the-art methods in various assessment indexes.


Saliency detection Manifold ranking K-means Graph model 



This work is supported by National Natural Science Foundation of China (NSFC) (61501260, 61471201, 61471203), Jiangsu Province Higher Education Institutions Natural Science Research Key Grant Project (13KJA510004), The peak of six talents in Jiangsu (RLD201402), and “1311 Talent Program” of NJUPT.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shan Su
    • 1
  • Ziguan Cui
    • 1
    Email author
  • Yutao Yao
    • 1
  • Zongliang Gan
    • 1
  • Guijin Tang
    • 1
  • Feng Liu
    • 1
  1. 1.Image Processing and Image Communication LabNanjing University of Posts and TelecommunicationsNanjingChina

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