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Saliency Detection via Objectness Transferring

  • Quan ZhouEmail author
  • Yawen Fan
  • Weihua Ou
  • Huimin Lu
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

In this paper, we present a novel framework to incorporate top-down guidance to identify salient objects. The salient regions/objects are predicted by transferring objectness prior without the requirement of center-biased assumption. The proposed framework consists of the following two basic steps: In the top-down process, we create a location saliency map (LSM), which can be identified by a set of overlapping windows likely to cover salient objects. The corresponding binary segmentation masks of training windows are treated as high-level knowledge to be transferred to the test image windows, which may share visual similarity with training windows. In the bottom-up process, a multi-layer segmentation framework is employed, providing local shape information that is used to delineate accurate object boundaries. Through integrating top-down objectness priors and bottom-up image representation, our approach is able to produce an accurate pixel-wise saliency map. Extensive experiments show that our approach achieves the state-of-the-art results over MSRA 1000 dataset.

Keywords

Salient object detection Objectness priors Location saliency map Multi-layer segmentation 

Notes

Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (Grant No. 61876093, 61881240048, 61571240, 61501247, 61501259, 61671253, 61762021), Natural Science Foundation of Jiangsu Province (Grant No. BK20181393, BK20150849, BK20160908), Huawei Innovation Research Program (HIRP2018), and Natural Science Foundation of Guizhou Province (Grant No. [2017]1130).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Engineering Research Center of Communications and NetworkingNanjing University of Posts & TelecommunicationsNanjingPeople’s Republic of China
  2. 2.State Key Lab. for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China
  3. 3.School of Big Data and Computer ScienceGuizhou Normal UniversityGuiyangPeople’s Republic of China
  4. 4.Department of Mechanical and Control EngineeringKyushu Institute of TechnologyKitakyushuJapan

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