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JACIII Vol.18 No.3 pp. 353-360
doi: 10.20965/jaciii.2014.p0353
(2014)

Paper:

Identification of Season-Dependent Sightseeing Spots Based on Metadata-Derived Features and Image Processing

Chia-Huang Chen and Yasufumi Takama

Graduate School of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

Received:
October 15, 2013
Accepted:
January 31, 2014
Published:
May 20, 2014
Keywords:
web mining, image classification, tourism informatics, season-dependent spot
Abstract

Sharing traveling experience and photos on Social Network Service or Web albums is more and more popular recently. Good sightseeing photos in specific situation such as sunset and spring season can impress tourists well, and be clues for them to consider where and when to visit for sightseeing. Regarding situations to be identified, this paper focuses on season. Compared with situations relating with weather and time of day (e.g., sunrise/sunset), whether or not different seasons have different scenery depends on sightseeing spots. Therefore, classifying sightseeing spots into season-dependent/independent is required as preprocessing for season-based classification of sightseeing photos. This paper proposes a hybrid approach for identifying season-dependent sightseeing spots, of which the first phase applies machine learning with statistical features of sightseeing photos obtained from metadata. In order to improve precision, the second phase applies color-based classification to spots identified as season-dependent in the first phase. The experimental results show the effectiveness of the proposed method.

Cite this article as:
C. Chen and Y. Takama, “Identification of Season-Dependent Sightseeing Spots Based on Metadata-Derived Features and Image Processing,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.3, pp. 353-360, 2014.
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References
  1. [1] D. Buhalis and R. Law, “Progress in information technology and tourism management: 20 years on and 10 years after the Internet – The state of eTourism research,” Tourism Management Vol.29, No.4, pp. 609-623, August 2008.
  2. [2] R. Law, S. Qi, and D. Buhalis, “Progress in tourism management: a review of website evaluation in tourism research,” Tourism Management, Vol.31, No.3, pp. 297-313, June 2010.
  3. [3] C. H. Chen and Y. Takama, “Situation-Oriented Hierarchical Classification for Sightseeing Images Based on Local Color Feature,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.17, No.3, pp. 459-468, April 2013.
  4. [4] C. H. Chen and Y. Takama, “Hybrid Approach of Using Visual and Tag Information for Situation-Oriented Clustering of Sightseeing Spot Images,” Conference on Technologies and Applications of Artificial Intelligence (TAAI), pp. 256-261, November 2012.
  5. [5] C. H. Chen and Y. Takama, “Situation-Oriented Clustering of Sightseeing Spot Images Using Visual and Tag Information,” Joint 6th Int. Conf. on Soft Computing and Intelligent Systems (SCIS) and 13th Int. Symp. on Advanced Intelligent Systems (ISIS), pp. 416-421, November 2012.
  6. [6] J. P. Lucas, N. Luz, M. N. Moreno, R. Anacleto, A. A. Figueiredo, and C. Martins, “A hybrid recommendation approach for a tourism system,” Expert Systems with Applications, Vol.40, No.9, pp. 3532-3550, 2013.
  7. [7] A. Vailaya, M. Figueiredo, A. Jain, and H. J. Zhang, “A Bayesian Framework for Semantic Classification of Outdoor Vacation Images,” Proc. SPIE Storage Retrieval Image Video Databases VII, Vol.3656, pp. 415-426, January 1999.
  8. [8] A. Vailaya, M. Figueiredo, A. Jain, and H. J. Zhang, “Image Classification for Content-Based Indexing,” IEEE Trans. on Image Processing, Vol.10, pp. 117-130, January 2001.
  9. [9] S. Papadopoulos, C. Zigkolis, G. Tolias, Y. Kalantidis, P. Mylonas, Y. Kompatsiaris, and A. Vakali, “Image Clustering through Community Detection on Hybrid Image Similarity Graphs,” IEEE Int. Conf. on Image Processing (ICIP), pp. 2353-2356, September 2010.
  10. [10] P. A. Moëllic, J. E. Haugeard, and G. Pittel, “Image clustering based on a shared nearest neighbors approach for tagged collections,” Proc. of the 2008 Int. Conf. on Content-based Image and Video Retrieval, pp. 269-278, July 2008.
  11. [11] H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recognition, Vol.34, No.12, pp. 2259-2281, December 2001.
  12. [12] J. F. Canny, “A Computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence,” Vol.8, pp. 679-698, 1986.
  13. [13] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, “The WEKA Data Mining Software: An Update,” SIGKDD Explorations, Vol.11, No.1, pp. 10-18, June 2009.
  14. [14] Y. EL-Manzalawy and V. Honavar, “WLSVM : Integrating LibSVM into Weka Environment,” Software available at
    http://www.cs.iastate.edu/˜yasser/wlsvm [Accessed May 13, 2013], 2005.
  15. [15] C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, 2:27:1–27:27, Software available at
    http://www.csie.ntu.edu.tw/˜cjlin/libsvm [Accessed May 13, 2013], 2011.

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