Abstract.
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 sub jective 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 oth- ers. In this paper, we treat the challenge of automatically inferring aes- thetic 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. Au- tomated classifiers are built using support vector machines and classifi- cation trees. Linear regression on polynomial terms of the features is also applied to infer numerical aesthetics ratings. The work attempts to ex- plore the relationship between emotions which pictures arouse in people, and their low-level content. Potential applications include content-based image retrieval and digital photography.