EAGER: Aesthetically Empowering Novice Photographers

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Digital photography and the Internet are enabling ordinary people to take and save photographs at negligible variable cost. As a result amateurs often accumulate large volumes of images. They are then confronted with the tedious chore of sorting through their massive collections to select the most aesthetically pleasing pictures. The onerous nature of this task is exacerbated by the sense of many people that articulating and quantifying aesthetic attributes is esoteric. Automated evaluation of photographs based on user-defined criteria is in a nascent stage, lacking the flexibility to provide a personalized experience tailored to the individual user. Current systems develop a set of classifiers based on an image dataset, but this approach impedes adaptation to user preferences and inhibits the flexibility needed to develop new heuristics for aesthetics. In this exploratory project the PI aims to raise the overall aesthetic quality of amateur photography by creating software to automatically sort and filter a collection of images using a set of analytically-constructed aesthetic principles, in a manner analogous to what is done by a professional photographer. The approach is based upon the concept of a hierarchal modular classifier that can be customized for different individuals, so that each user can have a classifier that characterizes his or her preferences. Since the classifier is a simple arrangement of modules, one can imagine that even a single user could design multiple classifiers that cater to his or her every preference; for example, the user could have a classifier to help select the best landscape photographs s/he has taken, and another to select the best portraits. Broader Impacts: Comparison of data taken from different demographics will enhance our understanding of how cultural, social, or other background factors influence individual preferences in aesthetics. The ability to tune image searches so as to return images that are more aesthetically pleasing according to specific criteria will have commercial implications (e.g., when companies endeavor to create designs, products, or services that are tailored to different audiences). The work has social networking applications as well, since the algorithms developed could enable people to find others with similar preferences. The PI's system will teach amateur photographers how to improve their picture-taking skills, by showing them specific possible modifications that could be applied to enhance their photographs. Project outcomes will lay the foundation for technology that can automatically post-process images so as to improve their aesthetic quality. Thus, this research ultimately will improve the overall quality of the images that are available on the Web.