Clustering Tourist Images using Caption Analysis - Understanding the Strengths of Tourist Destinations --Reference-Cited by-同舟云学术

Clustering Tourist Images using Caption Analysis - Understanding the Strengths of Tourist Destinations -

Published:2024-02-23 Issue: Volume:99 Page:499-504
ISSN:
Container-title:Proceedings of the 2024 9th International Conference on Intelligent Information Technology
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Author:

Tsujioka Suguru¹^ORCID,Watanabe Kojiro²^ORCID,Tsukamoto Akihiro²^ORCID

Affiliation:

1. Shikoku University, Japan

2. Tokushima University, Japan

Funder

JSPS KAKENHI

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3654522.3654597

Reference16 articles.

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2. Liu L. Zhang Y. Ma Z. & Wang H. (2023). An analysis on the spatiotemporal behavior of inbound tourists in Jiaodong Peninsula based on Flickr geotagged photos. International Journal of Applied Earth Observation and Geoinformation 120 103349.

3. Domènech, Antoni., Mohino, I., & Moya-Gómez, B. (2020). Using flickr geotagged photos to estimate visitor trajectories in world heritage cities. ISPRS International Journal of Geo-Information, 9(11), 646.

4. Han, Shanshan., Liu, C., Chen, K., Gui, D., & Du, Q. (2021). A tourist attraction recommendation model fusing spatial, temporal, and visual embeddings for flickr-geotagged photos. ISPRS International Journal of Geo-Information, 10(1), 20.

5. Suguru, Tsujioka., Kojiro, Watanabe., and Akihiro, Tsukamoto. (2023). Photo Classification Using Machine Learning to Understand the Interests of Tourists. In Information Systems for Intelligent Systems: Proceedings of ISBM 2022 (pp. 559-565). Singapore: Springer Nature Singapore.