Studying Aesthetics in Photographic Images
Using a Computational Approach

Ritendra Datta, Dhiraj Joshi, Jia Li, James Z. Wang
The Pennsylvania State University

Aesthetics, in the world of art and photography, refers to the principles of the nature and appreciation of beauty. Judging beauty and other aesthetic qualities of photographs is a highly subjective task. Hence, there is no unanimously agreed standard for measuring aesthetic value. In spite of the lack of firm rules, certain features in photographic images are believed, by many, to please humans more than certain others. In this paper, we treat the challenge of automatically inferring aesthetic quality of pictures using their visual content as a machine learning problem, with a peer-rated online photo sharing Website as data source. We extract certain visual features based on the intuition that they can discriminate between aesthetically pleasing and displeasing images. Automated classifiers are built using support vector machines and classification trees. Linear regression on polynomial terms of the features is also applied to infer numerical aesthetics ratings. The work attempts to explore the relationship between emotions which pictures arouse in people, and their low-level content. Potential applications include content-based image retrieval and digital photography.

Full color PDF file

On-line info   

Citation: Ritendra Datta, Dhiraj Joshi, Jia Li and James Z. Wang, ``Studying Aesthetics in Photographic Images Using a Computational Approach,'' Lecture Notes in Computer Science, vol. 3953, Proceedings of the European Conference on Computer Vision, Part III, pp. 288-301, Graz, Austria, May 2006.

Copyright 2006 Springer-verlag. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from Springer-verlag.

Last Modified: February 17, 2006.
2006, James Z. Wang