— http://en.wikipedia.org/wiki/Aesthetics
Since about 2005, computer scientists have attempted to develop automated methods to infer aesthetic quality of images.[53][54][55][56] Typically, these approaches follow a machine learning approach, where large numbers of manually rated photographs are used to “teach” a computer about what visual properties are of relevance to aesthetic quality. The Acquine engine, developed at Penn State University, rates natural photographs uploaded by users.[57]
Notable in this area is Michael Leyton, professor of psychology at Rutgers University. Leyton is the president of the International Society for Mathematical and Computational Aesthetics and the International Society for Group Theory in Cognitive Science and has developed a generative theory of shape.
There have also been relatively successful attempts with regard to chess and music.[58] A relation between Max Bense's mathematical formulation of aesthetics in terms of “redundancy” and “complexity” and theories of musical anticipation was offered using the notion of Information Rate.[59]
Datta, R.; Joshi, D.; Li, J.; Wang, J. (2006). “Studying aesthetics in photographic images using a computational approach”. Europ. Conf. on Computer Vision. Springer.
Wong, L.-K.; Low, K.-L. (2009). “Saliency-enhanced image aesthetic classification”. Int. Conf. on Image Processing. IEEE.
Wu, Y.; Bauckhage, C.; Thurau, C. (2010). “The good, the bad, and the ugly: predicting aesthetic image labels”. Int. Conf. on Pattern Recognition. IEEE.
Faria, J., Bagley, S., Rueger, S., Breckon, T.P. (2013). “Challenges of Finding Aesthetically Pleasing Images”. Proc. International Workshop on Image and Audio Analysis for Multimedia Interactive Services. IEEE. Retrieved 19 June 2013.
“Aesthetic Quality Inference Engine - Instant Impersonal Assessment of Photos”. Penn State University. Archived from the original on 9 May 2009. Retrieved 21 June 2009.
Manaris, B., Roos, P., Penousal, M., Krehbiel, D., Pellicoro, L. and Romero, J.; A Corpus-Based Hybrid Approach to Music Analysis and Composition; Proceedings of 22nd Conference on Artificial Intelligence (AAAI-07); Vancouver, BC; 839-845 2007.
59, Dubnov, S.; Musical Information Dynamics as Models of Auditory Anticipation; in Machine Audition: Principles, Algorithms and Systems, Ed. W. Weng, IGI Global publication, 2010.