Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
The Design of High-Level Features for Photo Quality Assessment
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Photo and Video Quality Evaluation: Focusing on the Subject
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
ACQUINE: aesthetic quality inference engine - real-time automatic rating of photo aesthetics
Proceedings of the international conference on Multimedia information retrieval
Classification of digital photos taken by photographers or home users
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
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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Photography requires not only equipment but also skill to reliably produce aesthetically-pleasing results. It can be argued that, for photography, skill is apparent even without sophisticated equipment. However, no scientific tests have been carried out to confirm that supposition. For that matter, there has been little scientific study on whether skill is apparent, whether it can be discerned by judges in blind tests. We report results of an experiment in which 33 subjects were asked to use identical cameras to photograph each of 7 pre-determined scenes, including a portrait, landscapes, and several man-made objects. Each photograph was then rated in a double-blind manner by 8 judges. Of those judges, 3 are professional photographic experts, and 5 are imaging researchers. The results show that expert judges are able to discern photographic skill to a statistically significant level, but that the enthusiasts, who are more akin to the general public, are not. We also analyse the photos using computer vision methods published in the literature, and find that there is no correlation between human judgements and the previously-published machine learning methods.