Correlating low-level image statistics with users - rapid aesthetic and affective judgments of web pages

Authors:
Xianjun Sam Zheng;Ishani Chakraborty;James Jeng-Weei Lin;Robert Rauschenberger
Affiliations:
Siemens Corporate Research, Princeton, NJ, USA;Siemens Corporate Research and Rutgers University, Princeton, NJ, USA;Siemens Corporate Research, Princeton, NJ, USA;Siemens Corporate Research and Simon Fraser University, Princeton, NJ, USA
Venue:
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Year:
2009

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Abstract

In this paper, we report a study that examines the relationship between image-based computational analyses of web pages and users' aesthetic judgments about the same image material. Web pages were iteratively decomposed into quadrants of minimum entropy (quadtree decomposition) based on low-level image statistics, to permit a characterization of these pages in terms of their respective organizational symmetry, balance and equilibrium. These attributes were then evaluated for their correlation with human participants' subjective ratings of the same web pages on four aesthetic and affective dimensions. Several of these correlations were quite large and revealed interesting patterns in the relationship between low-level (i.e., pixel-level) image statistics and design-relevant dimensions.