Abstract
So many people post photos as well as short messages to Twitter every minutes from everywhere on the earth. By monitoring the Twitter stream, we can obtain various kinds of images with texts. In this paper, as a case study of Twitter image mining for specific kinds of photos, we describe food photo mining from the Twitter stream. To collect food photos from Twitter, we monitor the Twitter stream to find the tweets containing both food-related keywords and photos, and apply a “foodness” classifier and 100-class food classifiers to them to verify whether they represent foods or not after downloading the corresponding photos. In the paper, we report the experimental results of our food photo mining for the Twitter photo data we have collected for two years and four months. As results, we detected about 470,000 food photos from Twitter. With this data, we made spatio-temporal analysis on food photos.
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Yanai, K., Kawano, Y. (2014). Twitter Food Photo Mining and Analysis for One Hundred Kinds of Foods. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, CK., Huet, B., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2014. PCM 2014. Lecture Notes in Computer Science, vol 8879. Springer, Cham. https://doi.org/10.1007/978-3-319-13168-9_3
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DOI: https://doi.org/10.1007/978-3-319-13168-9_3
Publisher Name: Springer, Cham
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