ABSTRACT
Over-tourism has become an important difficulty in Japan because the number of visiting international tourists has increased in recent years. This intensive tourism leads to sightseeing problems because opportunities to inform tourists about culture and rules in tourist areas are few. Some system is needed to convey correct cultural aspects of tourist areas. This paper proposes a system to present a user with useful information such as area- specific culture from photographs taken with a convolutional neural network (CNN). Tourists can gain information by associating the contents with the real world by browsing useful information while viewing photographs. After we constructed the prototype system to present 30 types of useful information in English, we evaluated our system quantitatively. We also administered a questionnaire survey for Japanese and foreign residents. The results demonstrate that our system is effective to facilitate foreign tourists' understanding Japanese culture and norms.
- A. M. Al Zubaidi-Polli and G. Anderst-Kotsis. Conceptual design of a hybrid participatory it supporting in-situ and ex-situ collaborative text authoring. In Proceedings of the 20th International Conference on Information Integration and Web-based Applications & Services, iiWAS2018, pages 243--252. ACM, 2018.Google ScholarDigital Library
- L. Cao, J. Luo, A. Gallagher, X. Jin, J. Han, and T. S. Huang. Aworldwide tourism recommendation system based on geotaggedweb photos. In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 2274--2277, 2010.Google ScholarCross Ref
- K. Cheverst, N. Davies, K. Mitchell, and A. Friday. Experiences of developing and deploying a context-aware tourist guide: The guide project. In Proceedings of the 6th Annual International Conference on Mobile Computing and Networking, MobiCom '00, pages 20--31. ACM, 2000.Google ScholarDigital Library
- B. Ferris, K. Watkins, and A. Borning. Onebusaway: Results from providing real-time arrival information for public transit. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '10, pages 1807--1816. ACM, 2010.Google ScholarDigital Library
- G. Gando, T. Yamada, H. Sato, S. Oyama, and M. Kurihara. Fine-tuning deep convolutional neural networks for distinguishing illustrations from photographs. Expert Syst. Appl., 66(C):295--301, Dec. 2016.Google Scholar
- L. Guo, Z. Li, and W. Sun. Understanding travel destination from structured tourism blogs. In Proceedings of 2015 Wuhan International Conference on e-Business, pages 144--151, 2015.Google Scholar
- R. Ji, X. Xie, H. Yao, and W.-Y. Ma. Mining city landmarks from blogs by graph modeling. In Proceedings of the 17th ACM International Conference on Multimedia, MM '09, pages 105--114. ACM, 2009.Google ScholarDigital Library
- Y. Kim, C. Oh, T. Lee, D. Lee, J. Lee, and B. Suh. Travel q: Questifying micro activities using travel photos to enhance travel experience. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, CHI EA '15, pages 1507--1512. ACM, 2015.Google ScholarDigital Library
- M. Li, J. Dai, S. Sahu, and M. Naphade. Trip analyzer through smartphone apps. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS '11, pages 537--540. ACM, 2011.Google ScholarDigital Library
- N. Liu, Y. Yuan, L. Wan, H. Huo, and T. Fang. A comparative study for contour detection using deep convolutional neural networks. In Proceedings of the 2018 10th International Conference on Machine Learning and Computing, ICMLC 2018, pages 203--208. ACM, 2018.Google Scholar
- K. Mitomi, M. Endo, M. Hirota, S. Yokoyama, Y. Shoji, and H. Ishikawa. How to find accessible free wi-fi at tourist spots in japan. In International Conference on Social Informatics, pages 389--403. Springer, 2016.Google ScholarDigital Library
- H. Nakahara, A. Jinguji, M. Shimoda, and S. Sato. An fpga-based fine tuning accelerator for a sparse cnn. In Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA '19, pages 186--186. ACM, 2019.Google ScholarDigital Library
- K. Okuyama and K. Yanai. A travel planning system based on travel trajectories extracted from a large number of geotagged photos on the web. In The era of interactive media, pages 657--670. Springer, 2013.Google ScholarCross Ref
- J. M. Parulian, K. M. Adhinugraha, and S. Alamri. Indoor navigation guidance for mobile device. In Proceedings of the 20th International Conference on Information Integration and Web-based Applications & Services, iiWAS2018, pages 345--349. ACM, 2018.Google ScholarDigital Library
- E. Rubegni, S. Gerardi, and M. Caporali. Mobile applications for helping users to keep track of their travel experience. In Proceedings of the 14th European Conference on Cognitive Ergonomics: Invent! Explore!, ECCE '07, pages 311--312. ACM, 2007.Google ScholarDigital Library
- A. V. Santana and J. Campos. Travel history: Reconstructing semantic trajectories based on heterogeneous social tracks sources. In Proceedings of the 22Nd Brazilian Symposium on Multimedia and the Web, Webmedia '16, pages 311--318. ACM, 2016.Google ScholarDigital Library
- C. Schaefer. Toward building a mobile app experience to support users' mobile travel needs. In Proceedings of the 2016 ACM SIGMIS Conference on Computers and People Research, SIGMIS-CPR '16, pages 17--18. ACM, 2016.Google ScholarDigital Library
- M. Toyoshima, M. Hirota, D. Kato, T. Araki, and H. Ishikawa. Where is the memorable travel destinations? In S. Staab, O. Koltsova, and D. I. Ignatov, editors, Social Informatics, pages 291--298. Springer International Publishing, 2018.Google ScholarCross Ref
- W. Wei, S. He, D. Wang, and Y. Yeboah. Multi-objective deep cnn for outdoor auto-navigation. In Proceedings of the 2018 2Nd International Conference on Deep Learning Technologies, ICDLT '18, pages 81--85. ACM, 2018.Google ScholarDigital Library
- X. Xie, D. Du, Q. Li, Y. Liang, W. T. Tang, Z. L. Ong, M. Lu, H. P. Huynh, and R. S. M. Goh. Exploiting sparsity to accelerate fully connected layers of cnn-based applications on mobile socs. ACM Trans. Embed. Comput. Syst., 17(2):37:1-37:25, Dec. 2017.Google ScholarDigital Library
- Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. M. Thalmann. Time-aware point-of-interest recommendation. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '13, pages 363--372. ACM, 2013.Google ScholarDigital Library
- P. Yugopuspito, I. M. Murwantara, and J. Sean. Mobile sign language recognition for bahasa indonesia using convolutional neural network. In Proceedings of the 16th International Conference on Advances in Mobile Computing and Multimedia, MoMM2018, pages 84--91. ACM, 2018.Google ScholarDigital Library
- A. M. A. Zubaidi-Polli, N. Verdezoto, N. A. Z. R-Smith, and G. Anderst-Kotsis. Ex-situ technology appropriation of an e-deliberation platform in an art gallery. In Proceedings of the 19th International Conference on Information Integration and Web-based Applications & Services, iiWAS '17, pages 343--352. ACM, 2017.Google ScholarDigital Library
Index Terms
- Tourism application with CNN-Based Classification specialized for cultural information
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