single-jc.php

JACIII Vol.17 No.4 pp. 573-580
doi: 10.20965/jaciii.2013.p0573
(2013)

Paper:

Spherical Visualization of Image Data with Clustering

Yuichi Yaguchi and Ryuichi Oka

The University of Aizu, Tsuruga, Ikkimachi, Aizuwakamatsu, Fukushima 965-8580, Japan

Received:
February 14, 2013
Accepted:
April 24, 2013
Published:
July 20, 2013
Keywords:
information visualization, data mining, knowledge discovery, multi-dimensional scaling, imagesimilarity
Abstract

This paper proposes to aid the search for images by visualization of the image data on a spherical surface. Many photographs were lost in the Tohoku tsunami, and those that were eventually found are now being scanned. However, the owners of the lost photographs are finding it difficult to search for their images within a large set of scanned images that contain no additional information. In this paper, we apply a spatial clustering technique called the Associated Keyword Space (ASKS) projected from a threedimensional (3D) sphere to a two-dimensional (2D) spherical surface for 2D visualization. ASKS supports clustering, and therefore, we construct an image search system in which similar images are clustered. In this system, similar images are identified by color inspection and by having similar characteristics. In this way, the system is able to support the search for images from within a huge number of images.

Cite this article as:
Yuichi Yaguchi and Ryuichi Oka, “Spherical Visualization of Image Data with Clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.4, pp. 573-580, 2013.
Data files:
References
  1. [1] Y. inc. “Flickr,”
    http://www.flickr.com/.
  2. [2] I. Bhattacharya and L. Getoor, “A Latent Dirichlet Model for Unsupervised Entity Resolution,” In SDM, 2006.
  3. [3] P. Bonacich, “Power and Centrality: A Family of Measures,” American J. of Sociology, Vol.92, No.5, pp. 1170-1182, 1987.
  4. [4] D. Lenat, “CYC: A large-scale investment in knowledge infrastructure,” Communications of the ACM, Vol.38, No.11, pp. 33-38, 1995.
  5. [5] G. Miller, R. Beckwith, C. Fellbaum, D. Gross, and K. Miller, “Introduction to wordnet: An on-line lexical database,” Int. J. of Lexicography, Vol.3, Issue 4, pp. 235-244, 1990.
  6. [6] B. Swartout, R. Patil, K. Knight, and T. Russ, “Toward distributed use of large-scale ontologies,” In Proc. of KAW96, 1996.
  7. [7] S. Borgatti, “Models of core/periphery structures,” Social Networks, Vol.21, Issue 4, pp. 375-395, Oct. 2000.
  8. [8] E. Sirin, J. Hendler, and B. Parsia, “Semi-automatic composition of web services using semantic descriptions,” In Proc. of WSMAI2003, pp. 17-24, 2003.
  9. [9] H. Poon and P. Domingos, “Unsupervised ontology induction from text,” Association for Computational Linguistics, In Proc. of ACL2010, pp. 296-305, 2010.
  10. [10] J. Seidenberg and A. Rector, “Web ontology segmentation: analysis, classification and use,” In Proc. of WWW2006, pp. 13-22, ACM, 2006.
  11. [11] P. Buitelaar, P. Cimiano, and B. Magnini, “Ontology learning from text: methods, evaluation and applications,” Ios Pr Inc, Vol.123, 2005.
  12. [12] P. J. Carrington, J. Scott, and S. Wasserman, “Models and methods in social network analysis,” Cambridge Univ Pr, 2005.
  13. [13] L. Getoor and C. P. Diehl, “Link mining: a survey,” SIGKDD Explor. Newsl., Vol.7, pp. 3-12, Dec. 2005.
  14. [14] G. Young and A. Householder, “Discussion of a set of points in terms of their mutual distances,” Psychometrika, Vol.3, No.1, pp. 19-22, 1938.
  15. [15] J. Kruskal, “Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis,” Psychometrika, Vol.29, No.1, pp. 1-27, 1964.
  16. [16] R. Shepard, “Multidimensional scaling: Theory and applications in the behavioral sciences,” Seminar Press New York, 1972.
  17. [17] H. Takahashi and R. Oka, “Self-organization an associated keyword space for text retrieval,” Proc. of WMSCI2001, pp. 302-307, Jul. 2001.
  18. [18] T. Komazawa and C. Hayashi, “Quantification Theory and Data Processing,” Tokyo, Asakura-shoten, 1982.
  19. [19] I. Wikimedia Foundation, “Wikipedia,”
    http://en.wikipedia.org/wiki/Main Page.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 05, 2021