Computer Science ›› 2020, Vol. 47 ›› Issue (11): 199-204.doi: 10.11896/jsjkx.190800145

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Sketch-based Image Retrieval Based on Attention Model

LI Zong-min1, LI Si-yuan1, LIU Yu-jie1, LI Hua2   

  1. 1 College of Computer & Communication Engineering,China University of Petroleum,Qingdao,Shandong 266580,China
    2 Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2019-08-28 Revised:2019-12-16 Online:2020-11-15 Published:2020-11-05
  • About author:LI Zong-min,born in 1965,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include computer graphics,image processing,and scienti-fic computing visualization.
    LI Si-yuan,born in 1996,postgraduate.His main research interests include computer vision,image processing,ima-ge retrieval,and sketch image recognition.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61379106,61379082,61227802) and Natural Science Foundation of Shandong Province (ZR2013FM036,ZR2015FM011).

Abstract: To solve the problems of the sparse features and the geometric distortion of hand-drawn images in the research field of SBIR (sketch based image retrieval),a new feature extraction method based on attention model is proposed in this paper.The retrieval results can be obtained efficiently and accurately by accurately extracting the semantic features of hand-drawn images.Firstly,convolutional neural network is used as the basic framework for extracting semantic features,and then the supervised training process is carried out.Attention model mechanism is introduced to locate effective semantic features by adding attention block after the last convolution layer of the convolution neural network,and the attention block is composed of spatial attention structure and channel attention structure.Finally,the final feature descriptor is formed by the fusion of semantic features in different layers,to realize high retrieval accuracy.The experimental results on benchmark Flickr15k dataset proves the feasibility and effectiveness of the proposed method.In addition,the proposed attention model can greatly improve the classification accuracy in the task of sketch classification.

Key words: Attention model, Convolutional neural network, Sketch classification, Sketch-based image retrieval

CLC Number: 

  • TP391.41
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