Visualizing Natural Image Statistics

Hui Fang, Gary Tam, Rita Borgo, Andrew Aubrey, Phil Grant, Paul Rosin, Christian Wallraven, Douglas Cunningham, David Marshall, Min Chen

Research output: Contribution to journalArticle (journal)peer-review

8 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1228-1241
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number7
Publication statusPublished - Jul 2013


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