Can a CNN Recognize Catalan Diet?

Nowadays, we can find several diseases related to the unhealthy diet habits of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In many cases, these diseases are related to the food consumption of people. Mediterranean diet is scientifically known as a healthy diet that helps to prevent many metabolic diseases. In particular, our work focuses on the recognition of Mediterranean food and dishes. The development of this methodology would allow to analise the daily habits of users with wearable cameras, within the topic of lifelogging. By using automatic mechanisms we could build an objective tool for the analysis of the patient's behaviour, allowing specialists to discover unhealthy food patterns and understand the user's lifestyle. With the aim to automatically recognize a complete diet, we introduce a challenging multi-labeled dataset related to Mediterranean diet called FoodCAT. The first type of label provided consists of 115 food classes with an average of 400 images per dish, and the second one consists of 12 food categories with an average of 3800 pictures per class. This dataset will serve as a basis for the development of automatic diet recognition. In this context, deep learning and more specifically, Convolutional Neural Networks (CNNs), currently are state-of-the-art methods for automatic food recognition. In our work, we compare several architectures for image classification, with the purpose of diet recognition. Applying the best model for recognising food categories, we achieve a top-1 accuracy of 72.29\%, and top-5 of 97.07\%. In a complete diet recognition of dishes from Mediterranean diet, enlarged with the Food-101 dataset for international dishes recognition, we achieve a top-1 accuracy of 68.07\%, and top-5 of 89.53\%, for a total of 115+101 food classes.

[1]  Johannes Fürnkranz,et al.  Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel , 2010, ICML.

[2]  Jian Dong,et al.  Collaborative Layer-Wise Discriminative Learning in Deep Neural Networks , 2016, ECCV.

[3]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[4]  Matthieu Guillaumin,et al.  Food-101 - Mining Discriminative Components with Random Forests , 2014, ECCV.

[5]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Petia Radeva,et al.  Simultaneous food localization and recognition , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[7]  Shengen Yan,et al.  Deep Image: Scaling up Image Recognition , 2015, ArXiv.

[8]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[9]  Keiji Yanai,et al.  A food image recognition system with Multiple Kernel Learning , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[10]  Filipe Monteiro-Silva,et al.  Olive oil's polyphenolic metabolites - from their influence on human health to their chemical synthesis , 2014, 1401.2413.

[11]  Masaki Aono,et al.  Food Image Recognition Using Covariance of Convolutional Layer Feature Maps , 2016, IEICE Trans. Inf. Syst..

[12]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[13]  Keiji Yanai,et al.  Automatic Expansion of a Food Image Dataset Leveraging Existing Categories with Domain Adaptation , 2014, ECCV Workshops.

[14]  Thomas S. Huang,et al.  Deep Networks for Image Super-Resolution with Sparse Prior , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[15]  Keiji Yanai,et al.  Image Recognition of 85 Food Categories by Feature Fusion , 2010, 2010 IEEE International Symposium on Multimedia.

[16]  Keiji Yanai,et al.  Recognition of Multiple-Food Images by Detecting Candidate Regions , 2012, 2012 IEEE International Conference on Multimedia and Expo.