Paper Info
Title
Social Media Image Recognition for Food Trend Analysis
Abstract
An increasing number of people share their thoughts and the images of their lives on social media platforms. People are exposed to food in their everyday lives and share on-line what they are eating by means of photos taken to their dishes. The hashtag #foodporn is constantly among the popular hashtags in Twitter and food photos are the second most popular subject in Instagram after selfies. The system that we propose, WorldFoodMap, captures the stream of food photos from social media and, thanks to a CNN food image classifier, identifies the categories of food that people are sharing. By collecting food images from the Twitter stream and associating food category and location to them, WorldFoodMap permits to investigate and interactively visualize the popularity and trends of the shared food all over the world.
Year
DOI
Venue
2017
10.1145/3077136.3084142
SIGIR
Keywords
Field
DocType
food image recognition, social media streaming, deep neural network
Image classifier,Trend analysis,World Wide Web,Internet privacy,Social media,Computer science,Popularity
Conference
ISBN
Citations 
PageRank 
978-1-4503-5022-8
1
0.35
References 
Authors
7
6
Name
Order
Citations
PageRank
Giuseppe Amato1505106.68
Paolo Bolettieri216615.13
vinicius monteiro de lira382.27
Cristina Ioana Muntean4328.28
Raffaele Perego51471108.91
Chiara Renso692576.04