Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (1): 136-143.doi: 10.11896/jsjkx.181202316

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Few-shot Food Recognition Combining Triplet Convolutional Neural Network with Relation Network

LV Yong-qiang1,2,MIN Wei-qing2,DUAN Hua1,JIANG Shu-qiang2   

  1. (College of Mathematics and System Science,Shandong University of Science and Technology,Qingdao,Shandong 266590,China)1;
    (Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China)2
  • Received:2018-12-14 Published:2020-01-19
  • About author:LV Yong-qiang,born in 1992,postgraduate.His main research interests include deep learning,computer vision and machine learning;DUAN Hua,born in 1976,Ph.D,professor.Her main research interests include Petri nets,process mining and machine learning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61472229,61602279,71704096,31671588),Sci. & Tech. Deve-lopment Fund of Shandong Province of China (2016ZDJS02A11,ZR2017BF015,ZR2017MF027),Humanities and Social Science Research Project of the Ministry of Education (16YJCZH154,16YJCZH041 16YJCZH012,18YJAZH017),Taishan Scholar Climbing Program of Shandong Pro-vince,and SDUST Research Fund (2015TDJH102).

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Abstract: Food recognition attracts wide attention in the fields of food health and smart home.Most existing work focuses on food recognition with large-scale labeled samples,thus failing to robustly recognize food categories with few samples,under this condition,few-shot food recognition is an urgent problem.Most metric learning based few-shot recognition methods emphasize more on the similarity values of the image pairs without paying substantial attention to the inter-class and intra-class variations.Most works mainly use triplet convolutional neural network with linear metric function to learn the inter-class and intra-class information,however the liner metric function is not discriminative enough for measuring similarities of food images.To address this problem,this paper used the learnable relation network as non-linear metric and proposed a triplet network with relation network to solve the above two disadvantages of the few-shot learning and triplet network.This model adopts triplet network as feature embedding network for the image feature learning and uses a relation network with better discrimination as the non-linearity metric to learn the inter-class and intra-class information.Also the proposed model is trained end-to-end.In addition,this paper proposed an on-line mining rule for triplet samples,which makes the model stable in the training stage.The comprehensive experi-mental was conducted on three food datasets,which are Food-101,VIREO Food-172 and ChineseFoodNet.Compared with popular few-shot learning methods,such as Relation network,Matching network,the proposed model achieves an average improvement of about 3.0%,and compared with triplet network with liner metric,it achieves an average improvement of about 1.0%.Also this paper explored the influence of the margin in the loss function,parameters setting of online triplet sampling and initialization methods on experiment performance.

Key words: Few-shot learning, Food recognition, Non-linear metric, Triplet network

CLC Number: 

  • TP391
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