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Food Recognition Based on Image Retrieval with Global Feature Descriptor

  • Wei SunEmail author
  • Xiaofeng Ji
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

This paper proposes a simple and effective non-parametric approach to solve the problem of food images parsing and label images with their categories. Firstly, the proposed approach works by six types of global image features: CEDD, FCTH, BTDH, EHD, CLD and SCD to matching with global image descriptors, labeling image with their categories, and the distance for each descriptor are fused to get the likelihood probability of each class, then efficient Markov random field (MRF) optimization is proposed for incorporating neighborhood context, besides optimization minimization are used Iterated Conditional Modes (ICM) algorithms. And this paper also introduces a non-parametric, data-driven approaches framework. This approach requires no training, just prior distribution and joint distribution are taken into account, and it can easily scale to data sets with tens of thousands of images and hundreds of labels. At last, the experiments show that the proposed method is significantly more accurate and faster at identifying food than existing methods.

Keywords

Automatic food recognition Global image descriptors Markov random field 

Notes

Acknowledgements

This work was supported by National Nature Science Foundation of China (NSFC) under Grants 61671356, 61201290.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Aerospace Science and TechnologyXidian UniversityXi’anChina
  2. 2.School of Aerospace Science and TechnologyXidian UniversityXi’anChina

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