1. INTRODUCTION
Regional differences are observed in the photos posted to Twitter, because peoples’ lifestyles differ from region to region. ● Since foods are essential to human life, the regional differences are expected to be larger. However, the regional difference on food images over Twitter has not been explored so far. We analyze regional food trends !
2. METHOD
[1] Classifying food and non-food photos
Fine-tuning ResNet  to prepare food/non-food classifier Classify food and To select only food images from raw Twitter photos | Food images UECFOOD100[2] Food101[3] Non-Food images ILSVRC2012 ImageNet Kawano et al [4] images
[2] Extracting food CNN features
Training of new CNN features by fine-tuning VGG16 with Triplet & cls. loss To extract food-specialized features which can discriminate small differences [3] Clustering and analyzing of regional tendency K-means clustering for the food images using the food CNN features classification cluster of the image into 17 food represent categories [4] Visualization of regional parts We find out which parts of the food images correspond to regional features by Grad-CAM [1]. To visualize regional features, we train region classifiers on some of the pre-defined food categories. .
3-1.EXPERIMENTS[1] : Twitter Geotagged Food Images
Selecting food images from Twitter images in 2016 for whole a year with the food/non-food classifier 190,000 food images from 3.78 million raw Twitter images East Asia   South Asia  South-East Asia North Europe Africa Oceania East Middle 3-2.EXPERIMENTS[2] : Trend Analysis
Middle East 11.8 % –  East Asia : “Noodles” and “Rice”, which are relatively rare in the other regions, are included at the top. South-East Asia : “Soup” is ranked at the top, and many dishes are made of vegetables and meats in soup. –  North America, South America, Europe : It turned out that 4 items of the top 5 items of the categories are the same –  Middle East : The top five food categories are the same as Europe. However there are many brown coffee photos which are a unique type to Middle East. Region Trend Analysis by Food Noodles 81.0 %
Salad East Asia 16.2 %
North America Soup South-East Asia 3.4 Noodles : East Asia’s ratio is the highest, many "ramen" noodles images are shown Salad : North America's ratio is the highest Soup : East-South Asia's ratio is the highest, using other categories of food Salad in North America  Soup in East-South Asia 3-3.EXPERIMENTS[3]: Visualization of Regional Features Example of clear regional features Meat in South-East Asia : Ball parts Meat in Middle East : Vegetable parts Meat in East Asia：Skewer parts Meat in North America：Flat parts
4 .CONCLUSIONS AND FUTURE WORK
•  We have analyzed regional food tendency on only the geo- tagged food images without using any textual information. •  Integration image data of the areas that could not be analyzed from the other photo SNSs such as Instagram and Weibo. [1] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In CVPR, 2016. [2] Y. Matsuda, H. Hoashi, and K. Yanai. Recognition of multiplefood images by detecting candidate regions. In ICME, 2012. [3] B. Lukas, G. Matthieu, and V.G. Luc. Food-101 – mining discriminative components with random forests. In ECCV, 2014. [4] Y. Kawano and K. Yanai. Automatic expansion of a food image dataset leveraging existing categories with domain adaptation. In ECCVWS, 2014 South America America 14.7 % 66.3