Food Detection by Fine-Tuning Pre-trained Convolutional Neural Network Using Noisy Labels |
Alshomrani, Shroog
(Computer Science Department, Umm Al-Qura University)
Aljoudi, Lina (Computer Science Department, Umm Al-Qura University) Aljabri, Banan (Computer Science Department, Umm Al-Qura University) Al-Shareef, Sarah (Computer Science Department, Umm Al-Qura University) |
1 | L. Bossard, M. Guillaumin, and L. Van Gool, "Food-101 - mining discriminative components with random forests," in European Conference on Computer Vision,2014. |
2 | H. Hassannejad, G. Matrella, P. Ciampolini, I. De Mu-nari, M. Mordonini, and S. Cagnoni, "Food image recognition using very deep convolutional networks," in Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management, 2016, pp. 41-49. |
3 | N. Martinel, G. L. Foresti, and C. Micheloni, "Wide-slice residual networks for food recognition," in 2018 IEEEWinter Conference on applications of computer vision (WACV). IEEE, 2018, pp. 567-576. |
4 | C. Liu, Y. Cao, Y. Luo, G. Chen, V. Vokkarane, M. Yunsheng, S. Chen, and P. Hou, "A new deep learning-based food recognition system for dietary assessment on an edge computing service infrastructure," IEEE Transactions on Services Computing, vol. 11, no. 2, pp. 249-261,2017. DOI |
5 | K. Yanai and Y. Kawano, "Food image recognition using deep convolutional network with pre-training and fine-tuning," in2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 2015, pp. 1-6. |
6 | Z. Fu, D. Chen, and H. Li, "Chinfood1000: A large benchmark dataset for Chinese food recognition," International Conference on Intelligent Computing. Springer, 2017, pp. 273-281. |
7 | G. Ciocca, P. Napoletano, and R. Schettini, "Cnn-based features for retrieval and classification of food images," Computer Vision and Image Understanding, vol. 176, pp.70-77, 2018. DOI |
8 | F. Cholletet al., "Keras," https://keras.io, 2015. |
9 | T. Kluyver, B. Ragan-Kelley, F. P'erez, B. E. Granger, M. Bussonnier, J. Frederic, K. Kelley, J. B. Hamrick,J. Grout, S. Corlayet al., Jupyter Notebooks-a publishing format for reproducible computational workflows., 2016, vol. 2016. |
10 | C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed,D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1-9. |
11 | D. Rolnick, A. Veit, S. Belongie, and N. Shavit, "Deep learning is robust to massive label noise," arXiv preprintarXiv:1705.10694, 2017. |
12 | M. Sandler, A. G. Howard, M. Zhu, A. Zhmoginov, and L. Chen, "Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation," CoRR, vol. abs/1801.04381,2018.[Online]. Available: http://arxiv.org/abs/1801.04381 |
13 | K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXivpreprint arXiv:1409.1556, 2014. |
14 | G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700-4708. |
15 | C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, andZ. Wojna, "Rethinking the inception architecture for computer vision," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp.2818-2826. |
16 | J. Li, T. Dai, Q. Tang, Y. Xing, and S.-T. Xia, "Cyclic annealing training convolutional neural networks for image classification with noisy labels," in2018 25th IEEE International Conference on Image Processing (ICIP).IEEE, 2018, pp. 21-25. |
17 | P. Chen, B. B. Liao, G. Chen, and S. Zhang, "Under-standing and utilising deep neural networks trained with noisy labels," in International Conference on MachineLearning. PMLR, 2019, pp. 1062-107 |
18 | D. de Ridder, F. Kroese, C. Evers, M. Adriaanse, and M. Gillebaart, "Healthy diet: Health impact, prevalence, correlates, and interventions," Psychology & health,vol. 32, no. 8, pp. 907-941, 2017. DOI |
19 | S. Mezgec and B. Korousic Seljak, "Nutrinet: a deep learning food and drink image recognition system for dietary assessment," Nutrients, vol. 9, no. 7, p. 657, 201 |
20 | J. Schmidhuber, "Deep learning in neural networks: An overview," Neural networks, vol. 61, pp. 85-117, 2015. DOI |
21 | D. H. Hubel and T. N. Wiesel, "Receptive fields, binocular interaction and functional architecture in the cat's visual cortex," The Journal of physiology, vol. 160, no. 1, pp. 106-154, 1962. DOI |
22 | P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, "Overfeat: Integrated recognition, localisation and detection using convolutional networks," arXiv preprint arXiv:1312.6229, 2013. |
23 | W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed,C.-Y. Fu, and A. C. Berg, "Ssd: Single shot multibox detector," in European conference on computer vision.Springer, 2016, pp. 21-37. |
24 | L. Zhou, C. Zhang, F. Liu, Z. Qiu, and Y. He, "Application of deep learning in food: a review," Comprehensive reviews in food science and food safety, vol. 18, no. 6, pp. 1793-1811, 2019. DOI |
25 | E. Arazo, D. Ortego, P. Albert, N. O'Connor, and K. McGuinness, "Unsupervised label noise modelling and loss correction," international Conference on MachineLearning. PMLR, 2019, pp. 312-321. |
26 | A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko,W. Wang, T. Wey and, M. Andreetto, and H. Adam, "Mobilenets: Efficient convolutional neural networks for mobile vision applications," arXivpreprintarXiv:1704.04861, 2017. |
27 | J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in2009 IEEE conference on computer vision and pattern recognition. Ieee, 2009, pp. 248-255. |
28 | E. J. Heravi, H. H. Aghdam, and D. Puig, "An optimised convolutional neural network with bottleneck and spatial pyramid pooling layers for classification of foods," Pattern Recognition Letters, vol. 105, pp. 50-58, 2018. DOI |
29 | H. Wu, M. Merler, R. Uceda-Sosa, and J. R. Smith, "Learning to make better mistakes: Semantics-aware visual food recognition," in Proceedings of the 24th ACM international conference on Multimedia, 2016, pp. 172-176. |
30 | P. Pandey, A. Deepthi, B. Mandal, and N. B. Puhan, "Foodnet: Recognising foods using an ensemble of deep networks," IEEE Signal Processing Letters, vol. 24, no. 12, pp. 1758-1762, 2017. DOI |
31 | C. Liu, Y. Cao, Y. Luo, G. Chen, V. Vokkarane, andY. Ma, "Deepfood: Deep learning-based food image recognition for computer-aided dietary assessment," in International Conference on Smart Homes and HealthTelematics. Springer, 2016, pp. 37-48. |
32 | A. Ramdani, A. Virgono, and C. Setianingsih, "Fooddetection with image processing using convolutional neural network (CNN) method," in 2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT). IEEE, 2020, pp.91-96. |
33 | J. Zheng, L. Zou, and Z. J. Wang, "Mid-level deep food part mining for food image recognition," IET ComputerVision, vol. 12, no. 3, pp. 298-304, 2018. |
34 | H. Kagaya, K. Aizawa, and M. Ogawa, "Food detection and recognition using convolutional neural network," in Proceedings of the 22nd ACM international conference on Multimedia, 2014, pp. 1085-1088. |