research-article

Food image recognition with deep convolutional features

Authors:

UbiComp '14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

Pages 589 - 593

https://doi.org/10.1145/2638728.2641339

Published: 13 September 2014 Publication History

Get Access

Abstract

In this paper, we report the feature obtained from the Deep Convolutional Neural Network boosts food recognition accuracy greatly by integrating it with conventional hand-crafted image features, Fisher Vectors with HoG and Color patches. In the experiments, we have achieved 72.26% as the top-1 accuracy and 92.00% as the top-5 accuracy for the 100-class food dataset, UEC-FOOD100, which outperforms the best classification accuracy of this dataset reported so far, 59.6%, greatly.

References

[1]

Arandjelovic, R., and Zisserman, A. Three things everyone should know to improve object retrieval. In Proc. of IEEE Computer Vision and Pattern Recognition (2012), 2911--2918.

Digital Library

Google Scholar

[2]

Bosch, M., Zhu, F., Khanna, N., Boushey, C. J., and Delp, E. J. Combining global and local features for food identification in dietary assessment. In Proc. of IEEE International Conference on Image Processing (2011).

Crossref

Google Scholar

[3]

Chatfield, K., Simonyan, K., Vedaldi, A., and Zisserman, A. Return of the devil in the details: Delving deep into convolutional nets. arXiv preprint arXiv:1405.3531 (2014).

Google Scholar

[4]

Chen, M., Yang, Y., Ho, C., Wang, S., Liu, S., Chang, E., Yeh, C., and Ouhyoung, M. Automatic chinese food identification and quantity estimation. In SIGGRAPH Asia 2012 Technical Briefs (2012).

Digital Library

Google Scholar

[5]

Crammer, K., Kulesza, A., and Dredze, M. Adaptive regularization of weight vectors. In Advances in Neural Information Processing Systems (2009), 414--422.

Google Scholar

[6]

Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. Decaf: A deep convolutional activation feature for generic visual recognition. arXiv preprint arXiv:1310.1531 (2013).

Google Scholar

[7]

Kawano, Y., and Yanai, K. Foodcam: A real-time food recognition system on a smartphone. Multimedia Tools and Applications (2014), 1--25.

Google Scholar

[8]

Krizhevsky, A., Sutskever, I., and Hinton, G. E. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (2012).

Digital Library

Google Scholar

[9]

Matsuda, Y., Hoashi, H., and Yanai, K. Recognition of multiple-food images by detecting candidate regions. In Proc. of IEEE International Conference on Multimedia and Expo (2012), 1554--1564.

Digital Library

Google Scholar

[10]

Perronnin, F., Sánchez, J., and Mensink, T. Improving the fisher kernel for large-scale image classification. In Proc. of European Conference on Computer Vision (2010).

Digital Library

Google Scholar

[11]

Yang, S., Chen, M., Pomerleau, D., and Sukthankar, R. Food recognition using statistics of pairwise local features. In Proc. of IEEE Computer Vision and Pattern Recognition (2010).

Google Scholar

Cited By

View all

Crystal AValero MNino VIngram K(2024)Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional EstimationSensors10.3390/s2413408924:13(4089)Online publication date: 24-Jun-2024
https://doi.org/10.3390/s24134089
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
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
https://doi.org/10.1134/S1547477124010059
Show More Cited By

Index Terms

Food image recognition with deep convolutional features
1. Applied computing
  1. Computers in other domains
    1. Personal computers and PC applications
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding

Recommendations

Food Detection and Recognition Using Convolutional Neural Network
MM '14: Proceedings of the 22nd ACM international conference on Multimedia

In this paper, we apply a convolutional neural network (CNN) to the tasks of detecting and recognizing food images. Because of the wide diversity of types of food, image recognition of food items is generally very difficult. However, deep learning has ...
Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model
MADiMa '16: Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management

Recent past has seen a lot of developments in the field of image-based dietary assessment. Food image classification and recognition are crucial steps for dietary assessment. In the last couple of years, advancements in the deep learning and ...
Food Image Recognition Using Very Deep Convolutional Networks
MADiMa '16: Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management

We evaluated the effectiveness in classifying food images of a deep-learning approach based on the specifications of Google's image recognition architecture Inception. The architecture is a deep convolutional neural network (DCNN) having a depth of 54 ...

Comments

Information & Contributors

Information

Published In

UbiComp '14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

September 2014

1409 pages

ISBN:9781450330473

DOI:10.1145/2638728

General Chairs:
AJ Brush
Microsoft Research
,
Adrian Friday
Lancaster University, UK
,
Program Chairs:
Julie Kientz
University of Washington
,
James Scott
Microsoft Research, UK
,
Junehwa Song
Korea Advanced Institute of Science and Technology (KAIST), KR

Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

In-Cooperation

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 September 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

UbiComp '14

Sponsor:

UbiComp '14: The 2014 ACM Conference on Ubiquitous Computing

September 13 - 17, 2014

Washington, Seattle

Acceptance Rates

Overall Acceptance Rate 764 of 2,912 submissions, 26%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

133
Total Citations
View Citations
1,371
Total Downloads

Downloads (Last 12 months)62
Downloads (Last 6 weeks)4

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Crystal AValero MNino VIngram K(2024)Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional EstimationSensors10.3390/s2413408924:13(4089)Online publication date: 24-Jun-2024
https://doi.org/10.3390/s24134089
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
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
https://doi.org/10.1134/S1547477124010059
Chhikara PChaurasia DJiang YMasur OIlievski F(2024)FIRE: Food Image to REcipe generation2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00800(8169-8179)Online publication date: 3-Jan-2024
https://doi.org/10.1109/WACV57701.2024.00800
Konstantakopoulos FGeorga EFotiadis D(2024)A Review of Image-Based Food Recognition and Volume Estimation Artificial Intelligence SystemsIEEE Reviews in Biomedical Engineering10.1109/RBME.2023.328314917(136-152)Online publication date: 2024
https://doi.org/10.1109/RBME.2023.3283149
Wyawahare MRane MPatil VTelkhade YTele RWalsepatil A(2024)Nutri Scan on Desk: A Food Sensitivity Testing System2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN)10.1109/ICIPCN63822.2024.00040(198-203)Online publication date: 3-Jul-2024
https://doi.org/10.1109/ICIPCN63822.2024.00040
Deeksha Meenpal TKhan MSahu M(2024)Automatic Food Billing System of Indian Food using YOLOv8 model2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT61001.2024.10725087(1-6)Online publication date: 24-Jun-2024
https://doi.org/10.1109/ICCCNT61001.2024.10725087
Liu MZhou BRey VBian SLukowicz P(2024)iEat: automatic wearable dietary monitoring with bio-impedance sensingScientific Reports10.1038/s41598-024-67765-514:1Online publication date: 2-Aug-2024
https://doi.org/10.1038/s41598-024-67765-5
Rokhva STeimourpour B(2024)Accurate & Real-time Food Classification through the Synergistic Integration of EfficientNetB7, CBAM, Transfer Learning, and Data AugmentationFood and Humanity10.1016/j.foohum.2024.100492(100492)Online publication date: Dec-2024
https://doi.org/10.1016/j.foohum.2024.100492
Phiphitphatphaisit SSurinta O(2024)Multi-layer adaptive spatial-temporal feature fusion network for efficient food image recognitionExpert Systems with Applications10.1016/j.eswa.2024.124834255(124834)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124834
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Food Detection and Recognition Using Convolutional Neural Network

Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model

Food Image Recognition Using Very Deep Convolutional Networks

Comments

Information

Published In

Sponsors

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations