This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Visualizing Natural Image Statistics
July 2013 (vol. 19 no. 7)
pp. 1228-1241
Hui Fang, Dept. of Comput. Sci., Swansea Univ., Swansea, UK
G. K-L Tam, Dept. of Comput. Sci., Swansea Univ., Swansea, UK
R. Borgo, Dept. of Comput. Sci., Swansea Univ., Swansea, UK
A. J. Aubrey, Dept. of Comput. Sci., Cardiff Univ., Cardiff, UK
P. W. Grant, Dept. of Comput. Sci., Swansea Univ., Swansea, UK
P. L. Rosin, Dept. of Comput. Sci., Cardiff Univ., Cardiff, UK
C. Wallraven, Dept. of Brain & Cognitive Eng., Korea Univ., Seoul, South Korea
D. Cunningham, Brandenburg Tech. Univ., Brandenburg, Germany
D. Marshall, Dept. of Comput. Sci., Cardiff Univ., Cardiff, UK
Min Chen, Oxford e-Res. Centre, Univ. of Oxford, Oxford, UK
Natural image statistics is an important area of research in cognitive sciences and computer vision. Visualization of statistical results can help identify clusters and anomalies as well as analyze deviation, distribution, and correlation. Furthermore, they can provide visual abstractions and symbolism for categorized data. In this paper, we begin our study of visualization of image statistics by considering visual representations of power spectra, which are commonly used to visualize different categories of images. We show that they convey a limited amount of statistical information about image categories and their support for analytical tasks is ineffective. We then introduce several new visual representations, which convey different or more information about image statistics. We apply ANOVA to the image statistics to help select statistically more meaningful measurements in our design process. A task-based user evaluation was carried out to compare the new visual representations with the conventional power spectra plots. Based on the results of the evaluation, we made further improvement of visualizations by introducing composite visual representations of image statistics.
Index Terms:
Visualization,Histograms,Principal component analysis,Kernel,Spectral analysis,Data visualization,Image color analysis,visual design,Image statistics,image visualization,usability study
Citation:
Hui Fang, G. K-L Tam, R. Borgo, A. J. Aubrey, P. W. Grant, P. L. Rosin, C. Wallraven, D. Cunningham, D. Marshall, Min Chen, "Visualizing Natural Image Statistics," IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 7, pp. 1228-1241, July 2013, doi:10.1109/TVCG.2012.312
Usage of this product signifies your acceptance of the Terms of Use.