JACIII Vol.17 No.3 pp. 459-468
doi: 10.20965/jaciii.2013.p0459


Situation-Oriented Hierarchical Classification for Sightseeing Images Based on Local Color Feature

Chia-Huang Chen and Yasufumi Takama

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

October 15, 2012
April 15, 2013
May 20, 2013
clustering, local color feature, ROI, K-means, sightseeing spot images

Nowadays, tourists take lots of photos and share them on album websites, so the meaningful grouping of images becomes important and useful. Specifically, sightseeing scenes vary with different situations such as weather and season. The categorization of different situations is thus expected to be beneficial to tourists planning when to visit different places. This paper proposes a hierarchical classification method based on local color feature extraction from the designed region of interest (ROI) and K-means clustering to categorize sightseeing images into several meaningful situations. Hierarchical organization consists of three stages and four situations. In the first stage, night-time images are discriminated from daytime images, then daytime images are divided into sunrise/sunset and other images in the second stage. Finally, cloudy images are separated from sunshiny images in other images obtained in the second stage. Experimental results show that the extraction of color features within the ROI is effective in obtaining clusters with high precision and recall.

Cite this article as:
Chia-Huang Chen and Yasufumi Takama, “Situation-Oriented Hierarchical Classification for Sightseeing Images Based on Local Color Feature,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.3, pp. 459-468, 2013.
Data files:
  1. [1] B. G. Prasad, K. K. Biswas, and S. K. Gupta, “Region-Based Image Retrieval using Integrated Color, Shape and Location Index,” Computer Vision and Image Understanding, Vol.94, pp. 193-233, 2004.
  2. [2] A. K. Jain and A. Vailaya, “Image Retrieval using Color and Shape,” Pattern Recognition, Vol.29, pp. 1233-1244, August 1996.
  3. [3] C. Carson, S. Belongie, H. Greenspan, and J. Malik, “Blobworld: Image Segmentation using Expectation-Maxinization and Its Application to Image Querying,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.24, pp. 1026-1038, August 2002.
  4. [4] A. B. Dahl and H. Aanaes, “Effective Image Database Search via Dimensionality Reduction,” IEEE Computer Conf. on Computer Vision and Pattern Recognition, pp. 1-6, June 2008.
  5. [5] R. C. Veltkamp and M. Tanase, “A Survey of Content-Based Image Retrieval Systems,” Multimedia Systems and Applications Series, Vol.21, pp. 47-101, 2002.
  6. [6] 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.
  7. [7] A. Vailaya, M. Figueiredo, A. Jain, and H. J. Zhang, “Content-based hierarchical classification of vacation images,” IEEE Int. Conf. on Multimedia Computing and Systems, Vol.1, pp. 518-523, July 1999.
  8. [8] D. Zhong, H. J. Zhang, and S. F. Chang, “Clustering methods for video browsing and annotation,” in Proc. SPIE Storage Retrieval Image Video Databases IV, Vol.2670, pp. 239-246, February 1996.
  9. [9] A. Vailaya, M. Figueiredo, A. Jain, and H. J. Zhang, “A Bayesian Framework for Semantic Classification of Outdoor Vacation Images,” in Proc. SPIE Storage Retrieval Image Video Databases VII, Vol. 3656, pp. 415-426, January 1999.
  10. [10] S. Silakari, M. Motwani, and M. Maheshwari, “Color Image Clustering using Block Truncation Algorithm,” Int. J. of Computer Science Issues (IJCSI), Vol.4, pp. 31-35, September 2009.
  11. [11] A. Sleit, A. L. A. Dalhoum, M. Qatawneh, M. Al-Sharief, R. Al-Jabaly, and O. Karajeh, “Image Clustering using Color, Texture and Shape Features,” KSII Trans. on Internet and Information Systems, Vol.5, pp. 211-227, January 2011.
  12. [12] W. T. Huang, “Affinity Propagation Based Image Clustering with SIFT and Color Features,” Master Thesis, Department of Computer Science, National Tsing Hua University, Taiwan, 2009.
  13. [13] J. Hartigan and M. Wong, “Algorithm as 136: A K-means Clustering Algorithm,” J. of the Royal Statistical Society, Series C (Applied Statistics), Vol.28, pp. 100-108, 1979.
  14. [14] J. Hartigan, “Clustering Algorithms,” JohnWiley & Sons, Inc., New York, 1975.
  15. [15] N. Zhou, W. M. Dong, J. X. Wang, and P. Jean-Claude, “Simulating Human Visual Perception in Nighttime Illumination,” Tsinghua Science & Technology, Vol.14, pp. 133-138, February 2009.
  16. [16] L. G. Liu, R. J. Chen, L. Wolf, and D. Cohen-Or, “Optimizing Photo Composition,” Computer Graphics Forum, Vol.29, pp. 469- 478, May 2010.
  17. [17] Rule of thirds (Wikipedia),,
    accessed on September 26, 2011.
  18. [18] J. F. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.8, pp. 679-698, 1986.
  19. [19] N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. on Systems, Man, and Cybernetics, Vol.9, pp. 62-66, January 1979.

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