Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (5): 125-133.doi: 10.11896/jsjkx.230300018

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Salient Object Detection Based on Feature Attention Purification

BAI Xuefei1, SHEN Wucheng1, WANG Wenjian1,2   

  1. 1 School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China
    2 Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education(Shanxi University),Taiyuan 030006,China
  • Received:2023-03-02 Revised:2023-08-17 Online:2024-05-15 Published:2024-05-08
  • About author:BAI Xuefei,born in 1980,Ph.D,asso-ciate professor,is a member of CCF(No.22413M).Her main research interests include image processing and machine learning.
    WANG Wenjian,born in 1968,Ph.D,professor,is a member of CCF(No.16143D).Her main research interests include image processing,machine learning and computing intelligence.
  • Supported by:
    National Natural Science Foundation of China(61703252,U21A20513,62076154,62276161),Key Research and Development Program of Shanxi Province(202102150401013) and Research Project Supported by Shanxi Scholarship Council of China(2022-008).

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Abstract: In recent years,salient object detection technology has made great progress,and how to select and effectively integrate multi-scale features plays an important role.Aiming at the information redundancy problem that may be caused by existing feature integration methods,a saliency detection model based on feature attention refinement is proposed.First,in the decoder,a global feature attention guidance module(GAGM) is used to process the deep features with semantic information through the attention mechanism to obtain global context information,and then these information is sent to each layer of the decoder for supervision through the global guidance flow train.The multi-scale features extracted by the encoder and the global context information are then effectively integrated using the multi-scale feature aggregation module(FAM),and further refined in the mesh feature purification module(MFPM) to generate clear and complete salient features.Experimental results on 5 public datasets demonstrate that the proposed model outperforms other existing saliency object detection methods.Besides,the processing speed of our approach is also very fast,it can run at a speed of more than 30 FPS when processing a 320 × 320 image.

Key words: Salient object detection, Attention mechanism, Multi-scale feature fusion, Feature selection, Mesh feature purification

CLC Number: 

  • TP391
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