The recognition of image is one of the most important fields in the image processing and computer vision. Image recognition has many branches but the food image classification is very unique. In today’s world people are very conscious about their health. Many people around the world use some dietary assessment system for planning of their diet. In dietary assessment system people make the use of food image classification to classify the food from the image. The classification of food images is a very difficult task as the dataset of food images is highly non-linear. In this paper, we proposed a method that can classify food images. We used pre trained models for the food image classification. The pre trained models is based on the convolutional neural network. In neural networks the CNNs is highly effective at the task of image classification and other computer vision problem. We classified a food image dataset i.e. food11 and obtained an accuracy of 96.75% in our experiment.

Deep Learning, CNN, Computer Vision, Image processing

[1] Md Tohidul Islam, Sagidur Rahman, B.M. Nafiz Karim Siddique, Taskeed Jabid, “Image Recognition with Deep Learning,” 2018 International Conference on Intelligent Informatics and Biomedical, Bangkok, Thailand, pp. 106-110, 2018.
[2] “Health effects of overweight and obesity in 195 countries over 25 years,”, New England Journal of Medicine 377, no. 1, 13–27, PMID: 28604169, 2017.
[3] Y. He, C. Xu, N. Khanna, C. J. Boushey and E. J. Delp, "Analysis of food images: Features and classification," IEEE International Conference on Image Processing (ICIP), Paris, pp. 2744-2748, 2014.
[4] Z. Zong, D. T. Nguyen, P. Ogunbona and W. Li, "On the Combination of Local Texture and Global Structure for Food Classification," IEEE International Symposium on Multimedia, Taichung, pp. 204- 211, 2010.
[5] S. J. Minija and W. R. S. Emmanuel, "Food image classification using sphere shaped — Support vector machine," International Conference on Inventive Computing and Informatics (ICICI), Coimbatore , pp. 109-113, 2017.
[6] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool, “Food-101 – mining discriminative components with random forests,” Computer Vision – ECCV (Cham) (David Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars, eds.), Springer International Publishing, pp. 446–461, 2014.
[7] Chang Liu, Yu Cao, Yan Luo, Guanling Chen, Vinod Vokkarane, and Yunsheng Ma, “Deepfood: Deep learning-based food image recognition for computer-aided dietary assessment,” CoRR abs/1606.05675, 2016.
[8] K. Yanai and Y. Kawano, "Food image recognition using deep convolutional network with pre-training and fine-tuning," IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Turin, pp. 1-6, 2015.
[9] N. Hnoohom and S. Yuenyong, "Thai fast food image classification using deep learning," International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON), Chiang Rai, pp. 116-119, 2018.
[10] G. Özsert Yi??i?t and B. M. Özyildirim, "Comparison of convolutional neural network models for food image classification," IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), Gdynia, pp. 349-353, 2017.
[11] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna, “Rethinking the inception architecture for computer vision,” CoRR abs/1512.00567 ,2015.
[12] J. Deng, W. Dong, R. Socher, L. J. Li, Kai Li and Li Fei-Fei, "ImageNet: A large-scale hierarchical image database," IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, pp. 248- 255, 2009.
[13] D. Ciregan, U. Meier and J. Schmidhuber, "Multi-column deep neural networks for image classification," IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, pp. 3642-3649, 2012.
[14] Kiyoharu Aizawa, Makoto Ogawa, “FoodLog: Smartphone based Multimedia Food Recording Tool” International Conference on Artificial Reality and Telexistence (ICAT), Tokyo, Japan, pp.143-144, 2013.
[15] Keigo Kitamura, Toshihiko Yamasaki, Kiyoharu Aizawa, “FoodLog: Capture, Analysis and Retrieval of Personal Food Images via Web” CEA, Beijing, China, pp. 23-29, 2009.
[16] Fengqing Zhu, Marc Bosch, Insoo Woo, SungYe Kim, Carol J. Boushey, David S. Ebert & Edward J. Del, “The Use of Mobile Devices in Aiding Dietary Assessment & Evaluation”, IEEE Journal of Selected Topics In Signal Processing, pp. 756-766, 2010.
[17] Christian Szegedy, Wei Liu, Yangqing Jio, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, “Going Deeper with Convolution”,CVPR, pp. 1-9, 2015.
[18] Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alexander A. Alemi, “Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning”, AAAI Conference on Artifical Intelligence, pp. 4278-4284, 2017.
[19] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, “Deep Residual Learning for Image Recognition”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.