title: Visualizing natural image statistics.
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.
authors: Department of Computer Science, Swansea University, Swansea SA2 8PP, Wales, United Kingdom. h.fang@swansea.ac.uk
authors: Hui
authors: H
authors: Fang
authors: Gary Kwok-Leung
authors: GK
authors: Tam
authors: Rita
authors: R
authors: Borgo
authors: Andrew J
authors: AJ
authors: Aubrey
authors: Philip W
authors: PW
authors: Grant
authors: Paul L
authors: PL
authors: Rosin
authors: Christian
authors: C
authors: Wallraven
authors: Douglas
authors: D
authors: Cunningham
authors: David
authors: D
authors: Marshall
authors: Min
authors: M
authors: Chen
mesh_terms: D000328:Adult; D000465:Algorithms; D000704:Analysis of Variance; D005260:Female; D006801:Humans; D008297:Male; D010775:Photic Stimulation; D014584:User-Computer Interface; D014796:Visual Perception; D055815:Young Adult