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Studying aesthetics in photographic images using a computational approach (2006)
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| Venue: | In Proc. ECCV |
| Citations: | 131 - 11 self |
BibTeX
@INPROCEEDINGS{Datta06studyingaesthetics,
author = {Ritendra Datta and Dhiraj Joshi and Jia Li and James Z. Wang},
title = {Studying aesthetics in photographic images using a computational approach},
booktitle = {In Proc. ECCV},
year = {2006},
pages = {7--13}
}
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Abstract
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 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. 1
Keyphrases
photographic image computational approach aesthetic quality numerical aesthetic rating low-level content subjective task certain others polynomial term peer-rated online photo certain feature classification tree content-based image retrieval machine learning problem certain visual feature firm rule aesthetic value work attempt digital photography support vector machine data source visual content linear regression potential application
