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The Visual Computer

, Volume 35, Issue 4, pp 473–488 | Cite as

Scene classification-oriented saliency detection via the modularized prescription

  • Chunlei Yang
  • Jiexin PuEmail author
  • Yongsheng Dong
  • Guo-sen Xie
  • Yanna Si
  • Zhonghua Liu
Original Article
  • 181 Downloads

Abstract

Saliency detection technology has been greatly developed and applied in recent years. However, the performance of current methods is not satisfactory in complex scenes. One of the reasons is that the performance improvement is often carried out through utilizing complicated mathematical models and involving multiple features rather than classifying the scene complexity and respectively detecting saliency. To break this unified detection schema for generating better results, we propose a method of scene classification-oriented saliency detection via the modularized prescription in this paper. Different scenes are described by a scene complexity expression model, and they are analyzed and discriminately detected by different pipelines. This process seems like that doctors can tailor the treatment prescriptions when they meet different symptoms. Moreover, two SVM-based classifiers are trained for scene classification and sky region identification, and the proposed sky region discrimination and erase model can be used to efficiently decrease the saliency interference by the high luminance of the background sky regions. Experimental results demonstrate the effectiveness and superiority of the proposed method in both higher precision and better smoothness, especially for detecting in structure complex scenes.

Keywords

Saliency detection Scene classification Modularized prescription Scene complexity expression Support Vector Machine (SVM) 

Notes

Acknowledgements

This work was supported in part by the International S & T Cooperation Program of Henan (No. 162102410021, 152102410036) and the National Natural Science Foundation of China (No. U1604153, U1504610).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Henan University of Science and TechnologyLuoyangChina

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