2017
Conference article  Restricted

Social Media Image Recognition for Food Trend Analysis

Amato G., Bolettieri P., Monteiro De Lira V., Muntean C. I., Perego R., Renso C.

Image recognition  Social media  Trend analysis 

n 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.

Source: SIGIR 2017 - 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1333–1336, Tokyo, Japan, 7 - 11 August, 2017

Publisher: ACM Press, New York, USA


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BibTeX entry
@inproceedings{oai:it.cnr:prodotti:381001,
	title = {Social Media Image Recognition for Food Trend Analysis},
	author = {Amato G. and Bolettieri P. and Monteiro De Lira V. and Muntean C. I. and Perego R. and Renso C.},
	publisher = {ACM Press, New York, USA},
	doi = {10.1145/3077136.3084142},
	booktitle = {SIGIR 2017 - 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1333–1336, Tokyo, Japan, 7 - 11 August, 2017},
	year = {2017}
}

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