research-article

Learning Multi-Subset of Classes for Fine-Grained Food Recognition

Authors:

Javier Ródenas,

Bhalaji Nagarajan,

Petia RadevaAuthors Info & Claims

MADiMa '22: Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management

Pages 17 - 26

https://doi.org/10.1145/3552484.3555754

Published: 24 October 2022 Publication History

Abstract

Food image recognition is a complex computer vision task, because of the large number of fine-grained food classes. Fine-grained recognition tasks focus on learning subtle discriminative details to distinguish similar classes. In this paper, we introduce a new method to improve the classification of classes that are more difficult to discriminate based on Multi-Subsets learning. Using a pre-trained network, we organize classes in multiple subsets using a clustering technique. Later, we embed these subsets in a multi-head model structure. This structure has three distinguishable parts. First, we use several shared blocks to learn the generalized representation of the data. Second, we use multiple specialized blocks focusing on specific subsets that are difficult to distinguish. Lastly, we use a fully connected layer to weight the different subsets in an end-to-end manner by combining the neuron outputs. We validated our proposed method using two recent state-of-the-art vision transformers on three public food recognition datasets. Our method was successful in learning the confused classes better and we outperformed the state-of-the-art on the three datasets.

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Cited By

Sheng GMin WZhu XXu LSun QYang YWang LJiang S(2024)A Lightweight Hybrid Model with Location-Preserving ViT for Efficient Food RecognitionNutrients10.3390/nu1602020016:2(200)Online publication date: 8-Jan-2024
https://doi.org/10.3390/nu16020200
Wang HTian HJu RMa LYang LChen JLiu F(2024)Nutritional composition analysis in food images: an innovative Swin Transformer approachFrontiers in Nutrition10.3389/fnut.2024.145446611Online publication date: 14-Oct-2024
https://doi.org/10.3389/fnut.2024.1454466
Yang YMin WSong JSheng GWang LJiang S(2024)Lightweight Food Recognition via Aggregation Block and Feature EncodingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/368028520:10(1-25)Online publication date: 22-Jul-2024
https://dl.acm.org/doi/10.1145/3680285
Show More Cited By

Index Terms

Learning Multi-Subset of Classes for Fine-Grained Food Recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition

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cover image ACM Conferences

MADiMa '22: Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management

October 2022

97 pages

ISBN:9781450395021

DOI:10.1145/3552484

General Chairs:
Stavroula Mougiakakou
University of Bern, Switzerland
,
Giovanni Maria Farinella
University of Catania, Italy
,
Keiji Yanai
The University of Electro-Communications, Tokyo, Japan
,
Dario Allegra
University of Catania, Italy

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SIGMM: ACM Special Interest Group on Multimedia

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Published: 24 October 2022

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European Union's Horizon 2020 Research and Innovation programme
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Generalitat de Catalunya

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MM '22

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MM '22: The 30th ACM International Conference on Multimedia

October 10, 2022

Lisboa, Portugal

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MADiMa '22 Paper Acceptance Rate 9 of 10 submissions, 90%;

Overall Acceptance Rate 16 of 24 submissions, 67%

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Cited By

Sheng GMin WZhu XXu LSun QYang YWang LJiang S(2024)A Lightweight Hybrid Model with Location-Preserving ViT for Efficient Food RecognitionNutrients10.3390/nu1602020016:2(200)Online publication date: 8-Jan-2024
https://doi.org/10.3390/nu16020200
Wang HTian HJu RMa LYang LChen JLiu F(2024)Nutritional composition analysis in food images: an innovative Swin Transformer approachFrontiers in Nutrition10.3389/fnut.2024.145446611Online publication date: 14-Oct-2024
https://doi.org/10.3389/fnut.2024.1454466
Yang YMin WSong JSheng GWang LJiang S(2024)Lightweight Food Recognition via Aggregation Block and Feature EncodingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/368028520:10(1-25)Online publication date: 22-Jul-2024
https://dl.acm.org/doi/10.1145/3680285
Sheng GMin WYao TSong JYang YWang LJiang S(2024)Lightweight Food Image Recognition With Global Shuffle ConvolutionIEEE Transactions on AgriFood Electronics10.1109/TAFE.2024.33867132:2(392-402)Online publication date: Sep-2024
https://doi.org/10.1109/TAFE.2024.3386713
Rodríguez-de-Vera JEstepa IBolaños MNagarajan BRadeva P(2024)LOFI: LOng-tailed FIne-Grained Network for Food Recognition2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00379(3750-3760)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00379
Xiao ZDiao GDeng Z(2024)Fine grained food image recognition based on swin transformerJournal of Food Engineering10.1016/j.jfoodeng.2024.112134380(112134)Online publication date: Nov-2024
https://doi.org/10.1016/j.jfoodeng.2024.112134
Nagarajan BBolaños MAguilar ERadeva P(2023)Deep ensemble-based hard sample mining for food recognitionJournal of Visual Communication and Image Representation10.1016/j.jvcir.2023.10390595:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.jvcir.2023.103905

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