demonstration

FoodCam-256: A Large-scale Real-time Mobile Food RecognitionSystem employing High-Dimensional Features and Compression of Classifier Weights

Authors:

Yoshiyuki Kawano,

Keiji YanaiAuthors Info & Claims

MM '14: Proceedings of the 22nd ACM international conference on Multimedia

Pages 761 - 762

https://doi.org/10.1145/2647868.2654869

Published: 03 November 2014 Publication History

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Abstract

In the demo, we demonstrate a large-scale food recognition system employing high-dimensional Fisher Vector and liner one-vs-rest classifiers. Since all the processes on image recognition perform on a smartphone, the system does not require an external image recognition server, and runs on an ordinary smartphone in a real-time way.

The proposed system can recognize 256 kinds of food by using the UEC-Food256 food image dataset we built by ourselves recently as a training dataset. To implement an image recognition system employing high-dimensional features on mobile devices, we propose linear weight compression method to save memory. In the experiments, we proved that the proposed compression methods make a little performance loss, while we can reduce the amount of weight vectors to 1/8. The proposed system has not only food recognition function but also the functions of estimation of food calorie and nutritious and recording a user's eating habits.

In the experiments with 100 kinds of food categories, we have achieved the 74.4% classification rate for the top 5 category candidates. The prototype system is open to the public as an Android-based smartphone application.

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References

[1]

Y. Matsuda, H. Hoashi, and K. Yanai, "Recognition of multiple-food images by detecting candidate regions," in Proc. of ICME, 2012.

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[2]

R Arandjelovic and A Zisserman, "Three things everyone should know to improve object retrieval," in CVPR, pp. 2911--2918, 2012.

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[3]

K Crammer, A Kulesza, and M. Dredze, "Adaptive regularization of weight vectors,"Machine Learning, vol. 91, no. 2, pp. 155--187, 2013.

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[4]

Y. Kawano and K. Yanai, "Rapid mobile object recognition using fisher vector," in Proc. of Asian Conference on Pattern Recognition, 2013.

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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
Chotwanvirat PPrachansuwan ASridonpai PKriengsinyos W(2024)Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping ReviewJournal of Medical Internet Research10.2196/5143226(e51432)Online publication date: 15-Nov-2024
https://doi.org/10.2196/51432
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
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Index Terms

FoodCam-256: A Large-scale Real-time Mobile Food RecognitionSystem employing High-Dimensional Features and Compression of Classifier Weights
1. Information systems
  1. Information systems applications

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Information

Published In

MM '14: Proceedings of the 22nd ACM international conference on Multimedia

November 2014

1310 pages

ISBN:9781450330633

DOI:10.1145/2647868

General Chairs:
Kien A. Hua
University of Central Florida, USA
,
Yong Rui
Microsoft Research, China
,
Ralf Steinmetz
Technische Universitt Darmstadt, Germany
,
Program Chairs:
Alan Hanjalic
Delft University of Technology, Netherlands
,
Apostol (Paul) Natsev
Google, USA
,
Wenwu Zhu
Tsinghua University, China

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 03 November 2014

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Japan Society for the Promotion of Science

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MM '14

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SIGMM

MM '14: 2014 ACM Multimedia Conference

November 3 - 7, 2014

Florida, Orlando, USA

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MM '14 Paper Acceptance Rate 55 of 286 submissions, 19%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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https://doi.org/10.3390/nu16020200
Chotwanvirat PPrachansuwan ASridonpai PKriengsinyos W(2024)Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping ReviewJournal of Medical Internet Research10.2196/5143226(e51432)Online publication date: 15-Nov-2024
https://doi.org/10.2196/51432
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
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Gerasimchuk MUzhinskiy A(2024)Food Recognition for Smart Restaurants and Self-Service CafesPhysics of Particles and Nuclei Letters10.1134/S154747712401005921:1(79-83)Online publication date: 12-Mar-2024
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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
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FoodCam: A real-time food recognition system on a smartphone

FoodCam: A Real-Time Mobile Food Recognition System Employing Fisher Vector

DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

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