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Natural Visualizations

Haroz, Steve and Ma, Dr. Kwan-Liu (2006) Natural Visualizations. [Conference Paper]

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Abstract

This paper demonstrates the prevalence of a shared characteristic between visualizations and images of nature. We have analyzed visualization competitions and user studies of visualizations and found that the more preferred, better performing visualizations exhibit more natural characteristics. Due to our brain being wired to perceive natural images [SO01], testing a visualization for properties similar to those of natural images can help show how well our brain is capable of absorbing the data. In turn, a metric that finds a visualization’s similarity to a natural image may help determine the effectiveness of that visualization. We have found that the results of comparing the sizes and distribution of the objects in a visualization with those of natural standards strongly correlate to one’s preference of that visualization.

Item Type:Conference Paper
Keywords:visualization, natural image, visualization perception
Subjects:Computer Science > Human Computer Interaction
ID Code:5671
Deposited By:Haroz, Mr. Steve
Deposited On:20 Aug 2007
Last Modified:11 Mar 2011 08:56

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Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

[Bra97] Brath R.: Metrics for effective information visualization. Proceedings of Information Visualization 1997 Symposium, 1997.

[BS02] Bederson B., Shneiderman B.: Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies. ACM Trans-actions on Graphics, 21(4), October 2002.

[FGP04] Fekete J. D., Grinstein G., Plaisant C.: IEEE InfoVis 2004 Contest, the history of InfoVis, (2004) http://www.cs.umd.edu/hcil/iv04contest

[Fie87] Field D. J.: Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America A, 4(12):2379–2394, December 1987.

[Fie93] Field D. J.: Scale-invariance and self-similar ’wavelet’ transforms: An analysis of natural scenes and mammalian visual systems. M. Farge, J. Hunt, and J. Vassilicos, editors. Wavelets, Fractals and Fourier transforms: New Development and New Applications, pages 151–193. Oxford University Press, 1993.

[GCD*05] Grinstein G., Cvek U., Derthick M., Trutschl M.: IEEE InfoVis 2005 Contest, Technology Data in the US, http://ivpr.cs.uml.edu/infovis05

[JS91] Johnson B., Shneiderman B.: Tree-maps: A spacefilling approach to the visualization of hierarchical information structures. Proceedings of IEEE Visualization 1991 Conference, pages 284–291, 1991.

[KL03] Karklin Y., Lewicki M.: Learning higher order structures in natural images. Network: Computation in Neural Systems, 14:483–499, 2003.

[Lan00] Langer M. S.: Large-scale failures of f -a scaling in natural image spectra. Journal of the Optical Society of America, 17:28-33, 2000.

[OF96] Olshausen B., Field D. J.: Natural image statistics and efficient coding. Network: Computation in Neural Systems, 7:333–339, 1996.

[PS88] Peitgen H., Saupe D. editors. The Science of Fractal Images. Springer-Verlag, New York, 1988.

[RST01] Reinhard E., Shirley P., Troscianko T.: Natural image statistics for computer graphics. Technical Report UUCS-01-002, School of Computing, University of Utah, March 2001.

[Rud97] Ruderman D.: Origins of scaling in natural images. Vision Research, 37(23):3385–3398, 1997.

[Sch92] Schroeder M.: Fractals, Chaos, Power Laws: Minutes from an Infinite Paradise. W.H. Freeman & Company, 1992.

[Sea95] Sears A.: Aide: A step toward metric-based development tools. ACM Symposium on User Interface Software and Technology, pages 101–110, 1995.

[Shn96] Shneiderman B.: The eyes have it: a task by data type taxonomy for information visualization. In Proceedings of the 1996 IEEE Symposium on Visual Languages, pages 336–343, 1996.

[SO01] Simoncelli E., Olshausen B.: Natural image statistics and neural representation. Annual Review of Neuroscience, 24:1193–1216, 2001.

[TO03] Torralba A., Oliva A.: Statistics of natural image categories. Network: Computation in Neural Systems, 14:391–412, 2003.

[Tuf90] Tufte E.: Envisioning Information. Graphics Press, Cheshire, CT, 1990.

[Wei] Weisstein E. W.: Power spectrum. MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/PowerSpectrum.html

[WTM05] Wang Y., Teoh S. T., Ma K. L.: Can Tree Visualization Really Discover Knowledge?. Tech. Report No. 2005-31, Department of Computer Science, University of California at Davis, December 2005

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