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IJMLC 2020 Vol.10(5): 624-629 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.5.983

Sightseeing Hot Spots Analysis by Using SNS’s Photos and Taking Location Information Based on Image-to-Tag Method

Kohjiro Hashimoto, Tadashi Miyosawa, Mai Miyabe, Takeshi Tsuchiya, Takeshi Ozaki, and Hiroo Hirose

Abstract—The many sightseeing photos are posted in Social Network Service (SNS). Any interested objects for tourists are shown up in the posted photos. Therefore, it is considered that the characteristics of visit spot can be discovered by analyzing the SNS’s photos. By the way, Microsoft started the cloud service of an image analysis based on Artificial Intelligence technology. The Computer Vision API in this cloud service makes image segmentation and object labeling automatically possible for SNS’s photos. Therefore, it is considered that it is possible to detect the characteristics of visit spot as label information by analyzing statistically generated labels based on Computer Vision API. In this paper, an analysis method of the generated tags based on Computer Vision API is studied on. Concretely, we propose a calculation method of the degree of interest for the generated tags, and an analysis method of interest object in each region based on both the tag’s interest degree and taking location information of SNS’s photos.

Index Terms—Tourism informatics, data mining, SNS image analysis, deep learning.

Kohjiro Hashimoto, Tadashi Miyosawa, Mai Miyabe, Takeshi Tsuchiya, Takeshi Ozaki, and Hiroo Hirose are with the Department of Applied Information Engineering, Suwa University of Science, 5000-1, Chino, Nagano, Japan (e-mail: k-hashimoto@rs.sus.ac.jp).

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Cite: Kohjiro Hashimoto, Tadashi Miyosawa, Mai Miyabe, Takeshi Tsuchiya, Takeshi Ozaki, and Hiroo Hirose, "Sightseeing Hot Spots Analysis by Using SNS’s Photos and Taking Location Information Based on Image-to-Tag Method," International Journal of Machine Learning and Computing vol. 10, no. 5, pp. 624-629, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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