Fang, H, Tam, GK-L, Borgo, R, Aubrey, AJ, Grant, PW, Rosin, PL, Wallraven, C, Cunningham, D, Marshall, D and Chen, M (2012) Visualizing Natural Image Statistics. IEEE Transactions on Visualization and Computer Graphics, 19 (7). pp. 1228-1241. ISSN 1077-2626

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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.

Item Type:	Article
Uncontrolled Keywords:	0801 Artificial Intelligence And Image Processing, 0802 Computation Theory And Mathematics
Subjects:	H Social Sciences > HA Statistics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science & Mathematics
Publisher:	IEEE
Date Deposited:	25 Jun 2018 11:33
Last Modified:	04 Sep 2021 02:36
DOI or ID number:	10.1109/TVCG.2012.312
URI:	https://researchonline.ljmu.ac.uk/id/eprint/8892

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