Food Recognition System for Nutrition Monitoring

• Charles N. C. Freitas UFRPE
• Filipe R. Cordeiro UFRPE
• Adenilton J. da Silva UFRPE

DOI: https://doi.org/10.5753/eniac.2018.4415

Resumo

This research consists of the analysis of the methods of image recognition, focusing on the problem of food classification, aiming to use the methods in a mobile application for the assistance in food monitoring and control. Thus, the development of the work contemplates the use of the deep learning method, focused on the recognition of food in images, with the use of neural convolution networks (CNN). For this purpose, a data set consisting of more than 1000 images and 5 food classes was constructed in order to simulate the SimpleNet, MiniVGGNet and Small Xception models, and thus define a learning model for food classification.

Referências

[Bossard et al. 2014] Bossard, L., Guillaumin, M., and Van Gool, L. (2014). Food-101 – mining discriminative components with random forests. In European Conference on Computer Vision.

[Chollet 2016] Chollet, F. (2016). Building powerful image classification models using very little data. Available at https://blog.keras.io/building-powerful-image-classificationmodels-using- very-little-data.html.

[Chollet 2017] Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions.

[Deshpande 2016] Deshpande, A. (2016). A beginner’s guide to understanding convolutional neural networks. Available at https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner’s-Guide-To- Understanding-Convolutional-Neural-Networks/.

[Ferreira 2014] Ferreira, B. M. (2014). Usabilicity: um jogo de apoio ao ensino de propriedades de usabilidade de software através de analogias. Technical report, XXV Simpósio Brasileiro de Informática na Educação – SBIE.

[Gomes 2013] Gomes, M. (2013). De dieta em dieta: o que a ciência diz sobre as soluções milagrosas. ComCiência, Campinas. Available at http://comciencia.scielo.br.

[Goodfellow et al. 2016] Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press. http://www.deeplearningbook.org.

[IBGE 2010] IBGE (2010). Censo do instituto brasileiro de geografia e estatística. Available at http://censo2010.ibge.gov.br.

[Kagaya et al. 2014] Kagaya, H., Aizawa, K., and Ogawa, M. (2014). Food detection and recognition using convolutional neural network.

[Moujahid 2016] Moujahid, A. (2016). A practical introduction to deep learning with caffe and python. Available at http://adilmoujahid.com/posts/2016/06/introduction-deeplearning-python-caffe/.

[Myers et al. 2016] Myers, A., Johnston, N., Rathod, V., Korattikara, A., Gorban, A., Silberman, N., Guadarrama, S., Papandreou, G., Huang, J., and Murphy, K. (2016). Im2calories: towards an automated mobile vision food diary.

[Ng et al. 2013] Ng, A., Ngiam, J., Foo, C. Y., Mai, Y., Suen, C., Coates, A., Maas, A., Hannun, A., Huval, B., Wang, T., and Tandon, S. (2013). Deep learning tutorial. Technical report, Stanford.

[Nielsen 2012] Nielsen, J. (2012). Usability 101: Introduction to usability. Available at http://www.nngroup.com/articles/usability-101-introduction-to-usability.

[Rodrigues 2013] Rodrigues, P. M. P. (2013). Reconhecimento automático de calorias numa refeição. Master’s thesis, Faculdade de Ciências da Universidade do Porto.

[SAÚDE 2016] SAÚDE,M. D. (2016). Ha´bitos dos brasileiros impactamno crescimento da obesidade e aumenta prevalência de diabetes e hipertens˜ao. Technical report, VIGITEL BRASIL.

[Simonyan and Zisserman 2014] Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. Visual Geometry Group, Department of Engineering Science, University of Oxford.

[Solomatine et al. 2008] Solomatine, D., See, L. M., and Abrahart, R. J. (2008). Data-driven modelling: Concepts, approaches and experiences. Springer-Verlag Berlin Heidelberg.

[SOUZA 2010] SOUZA, E. B. (2010). Transição nutricional no brasil: análise dos principais fatores. Cadernos UniFOA. Volta Redonda, Ano V, n. 13.

[Team 2017] Team, D. D. (2017). Deeplearning4j: Open-source distributed deep learning for the jvm, apache software foundation license 2.0. Available at http://deeplearning4j.org.

[Wang and Perez 2017] Wang, J. and Perez, L. (2017). The effectiveness of data augmentation in image classification using deep learning. CORNELL UNIVERSITY LIBRARY.

[Y. LeCun and Hinton 2015] Y. LeCun, Y. B. and Hinton, G. (2015). Deep learning. NATURE — VOL 521.