Computer Science ›› 2020, Vol. 47 ›› Issue (11): 179-185.doi: 10.11896/jsjkx.190900008

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Salient Object Detection Based on Multi-scale Deconvolution Deep Learning

WEN Jing, LI Yu-meng   

  1. School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China
  • Received:2019-09-02 Revised:2020-03-27 Online:2020-11-15 Published:2020-11-05
  • About author:WEN Jing,born in 1982,Ph.D,associate professor,M.S.supervisor,is a member of China Computer Federation.Her main research interests include compu-ter vision,image processing and pattern recognition.
  • Supported by:
    This paper was supported by the Young Scientists Fund of the National Natural Science Foundation of China (61703252),1331 Engineering Project of Shanxi Province and Shanxi Province Applied Basic Research Programs(201701D121053).

Abstract: Saliency detection aims to highlight the regional objects that people pay attention to subjectively in images.However,the traditional methods mainly distinguish the objects against the background under single resolution,so it's a hard to obtain the local detailed information under various scale.In this paper,we proposed a multi-scale convolution-combined-deconvolution network model.More specifically,we applied the deconvolution on the feature layers as well as their contract features,so that more multi-scale parameters could be maintained;then the fusion of the deconvolution offsets were combined with global information to get the salient result.The experimental results show that with many uncertainty factors in the complex background,compared with traditional methods,the proposed method could get a satisfactory salient detection,Compared with the latest deep learning methods,there can be relatively clear and accurate areas,which reduces the loss of information to some extent and restores more details,at the same time,the runtime of our method has been accelerated due to the design of the independence between the deconvolution layers.

Key words: Deconvolution, Deep learning, Multiresolution, Multi-Scale features, Saliency detection

CLC Number: 

  • TP391.41
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