Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TVCG.2012.312 | IEEE Trans. Vis. Comput. Graph. |
Keywords | Field | DocType |
correlation analysis,statistical distributions,visualizing natural image statistics,cognition,image representation,cognitive science,symbolism,image visualization,new visual representation,different category,visual abstraction,image statistic,task-based user evaluation,statistical analysis,deviation analysis,natural image statistics visualization,natural image statistic,image category,usability study,categorized data,visual representation,composite visual representation,data visualisation,natural scenes,computer vision,power spectrum,anova,distribution analysis,image statistics,conventional power spectra plot,statistical information,statistical result visualization,power spectra,visual design,data visualization,visualization,histograms,visual perception,algorithms,principal component analysis,analysis of variance,kernel,young adult | Histogram,Computer science,Theoretical computer science,Probability distribution,Artificial intelligence,Kernel (linear algebra),Computer vision,Data visualization,Communication design,Visualization,Design process,Statistics,Principal component analysis | Journal |
Volume | Issue | ISSN |
19 | 7 | 1941-0506 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Fang | 1 | 114 | 14.47 |
| Gary Kwok-Leung Tam | 2 | 263 | 14.23 |
| Rita Borgo | 3 | 266 | 18.44 |
| Andrew J Aubrey | 4 | 9 | 1.23 |
| Philip W Grant | 5 | 38 | 5.71 |
| Paul L. Rosin | 6 | 2559 | 254.25 |
| Christian Wallraven | 7 | 700 | 94.06 |
| Douglas Cunningham | 8 | 74 | 6.16 |
| David Marshall | 9 | 1133 | 106.45 |
| Min Chen | 10 | 1293 | 82.69 |