• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

Computer Engineering & Science

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A garment retrieval method based on deformable convolution

WANG Zhen,QUAN Hong-yan   

  1.  (School of Computer Science and Technology,East China Normal University,Shanghai 200062,China)
     
     
  • Received:2018-11-27 Revised:2019-01-08 Online:2019-09-25 Published:2019-09-25

Abstract:

Traditional garment retrieval methods use fixed-shape receptive fields, and they cannot extract features effectively when the garment target has geometric deformation. To solve this problem, we propose a garment retrieval method based on deformable convolution and similarity learning. Firstly, we build a deformable convolutional network which can automatically learn the sampling locations of garment features and the Hash code of garment images. Secondly, a similarity learning network is cascaded to measure the similarity of the Hash code. Finally, we obtain the retrieval results according to similarity scores. Experimental results show that this method can effectively extract the features of garment objects with geometric deformation, thus reducing the impact of image background features and improving the accuracy of the retrieval model.
 

Key words: garment retrieval, deformable convolution, Hash code, similarity learning