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IEEE Transactions on Pattern Analysis and Machine Intelligence
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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International Journal of Computer Vision
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ACQUINE: aesthetic quality inference engine - real-time automatic rating of photo aesthetics
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Saliency-enhanced image aesthetics class prediction
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A framework for photo-quality assessment and enhancement based on visual aesthetics
Proceedings of the international conference on Multimedia
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
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CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
High level describable attributes for predicting aesthetics and interestingness
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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We propose and demonstrate a strategy to quantify aesthetic quality in photographs. Our approach is to develop a small set of classification features by tuning general compositional principles to a targeted image domain where saliency can be better understood. We demonstrate this strategy with photographic portraits of individuals, but it can be extended to other domains. Our technique leverages a refined method of using templates as spatial composition feature look-up tables. Compared to the traditional approach using a large set of global and local features extracted with little salient knowledge, classifiers using features extracted with our approach are better predictors of human aesthetic judgments.