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14 March 2014 Identifying image preferences based on demographic attributes
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Proceedings Volume 9014, Human Vision and Electronic Imaging XIX; 90140T (2014)
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States
The intent of this study is to determine what sorts of images are considered more interesting by which demographic groups. Specifically, we attempt to identify images whose interestingness ratings are influenced by the demographic attribute of the viewer’s gender. To that end, we use the data from an experiment where 18 participants (9 women and 9 men) rated several hundred images based on “visual interest” or preferences in viewing images. The images were selected to represent the consumer “photo-space” - typical categories of subject matter found in consumer photo collections. They were annotated using perceptual and semantic descriptors. In analyzing the image interestingness ratings, we apply a multivariate procedure known as forced classification, a feature of dual scaling, a discrete analogue of principal components analysis (similar to correspondence analysis). This particular analysis of ratings (i.e., ordered-choice or Likert) data enables the investigator to emphasize the effect of a specific item or collection of items. We focus on the influence of the demographic item of gender on the analysis, so that the solutions are essentially confined to subspaces spanned by the emphasized item. Using this technique, we can know definitively which images’ ratings have been influenced by the demographic item of choice. Subsequently, images can be evaluated and linked, on one hand, to their perceptual and semantic descriptors, and, on the other hand, to the preferences associated with viewers’ demographic attributes.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Elena A. Fedorovskaya and Daniel R. Lawrence "Identifying image preferences based on demographic attributes", Proc. SPIE 9014, Human Vision and Electronic Imaging XIX, 90140T (14 March 2014);


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