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| ■論文No. |
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| ■ページ数 |
9ページ |
■発行日
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2020/08/01 |
| ■タイトル |
来遊者に興味を与える地域特性を発見するためのComputer Vision APIに基づくSNS投稿画像分析 |
| ■タイトル(英語) |
Analytical Method of SNS's Images Based on Computer Vision API to Discover Geographical Characteristics |
| ■著者名 |
橋本 幸二郎（公立諏訪東京理科大学 工学部情報応用工学科），三代沢 正（公立諏訪東京理科大学 工学部情報応用工学科），宮部 真衣（公立諏訪東京理科大学 工学部情報応用工学科），土屋 健（公立諏訪東京理科大学 工学部情報応用工学科），尾崎 剛（公立諏訪東京理科大学 工学部情報応用工学科），広瀬 啓雄（公立諏訪東京理科大学 工学部情報応用工学科） |
| ■著者名(英語) |
Kohjiro Hashimoto (Deptartmen of Applied Information Enginneering, Suwa University of Science), Tadashi Miyosawa (Deptartmen of Applied Information Enginneering, Suwa University of Science), Mai Miyabe (Deptartmen of Applied Information Enginneering, Suwa University of Science), Takeshi Tsuchiya (Deptartmen of Applied Information Enginneering, Suwa University of Science), Takeshi Ozaki (Deptartmen of Applied Information Enginneering, Suwa University of Science), Hiroo Hirose (Deptartmen of Applied Information Enginneering, Suwa University of Science) |
| ■価格 |
会員 ¥550 一般 ¥770 |
| ■書籍種類 |
論文誌(論文単位) |
| ■グループ名 |
【C】電子・情報・システム部門 |
| ■本誌 |
電気学会論文誌C（電子・情報・システム部門誌） Vol.140 No.8 （2020） 特集Ⅰ：社会課題解決に向けた超スマート社会実現技術 特集Ⅱ：国際会議ICESS 2019
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| ■本誌掲載ページ |
916-924ページ |
| ■原稿種別 |
論文／日本語 |
| ■電子版へのリンク |
https://www.jstage.jst.go.jp/article/ieejeiss/140/8/140_916/_article/-char/ja/
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| ■キーワード |
観光情報学，SNS画像分析，深層学習 tourism informatics，SNS image analysis，deep learning |
| ■要約(日本語) |
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| ■要約(英語) |
The influence of foreign tourists on the Japanese economy is significant. However, the number of tourists have grown at a sluggish pace in local region. In local region, there are attractive features not found in urban areas, and there is possibility that hidden information can be transmitted in addition to the existing famous tourist information. Therefore, there is a demand from tourist operators to discover the potential needs of their area.In this paper, we propose a method for analyzing objects that the area has given interest to visitors from images posted on the social networking service to discover potential needs in the region. The feature of this method is that it uses an image analysis service based on deep learning opened on the cloud service. This image analysis service can recognize the objects in a image and output their names as tag information. Therefore, by collecting SNS's images in any region, converting then into tag information, and statistically analyzing the tag information, the objects that the region is interested in is extracted as tag information. In this paper, we propose a analysis method of tag information based on the frequency of appearance tags and based on the difference between appearance tags in other regions. And the effectiveness was verified through the several experiments. In general, deep learning technology requires collection of large amount images and labeling for learning data in order to construct an image recognition model. In addition, in order to learn the model, it is necessary to prepare high spec computer environment. When introducing the analysis system of SNS's images, it is difficult to request them from tourist operators. On the other hand, the model of cloud service will be updated year by year, and the accuracy and versatility will be improved. Therefore, the effectiveness of using cloud services to analyze SNS's images is clarified in this paper. |
| ■版 型 |
A4 |
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