ACQUINE (Aesthetic Quality Inference Engine) is a machine-learning based online system of computer-based prediction of aesthetic quality for color natural photographic pictures. In our opinion, even though Acquine has much room for improvements, this is an important step in the intersection of computer science research and the arts because it shows that computers can learn about and exhibit "emotional responses" to visual stimilus like humans do. It has been developed at Penn State since about 2005. Dr. Ritendra Datta (now with the Google engineering office in Pittsburgh) was the main developer, working with Prof. Jia Li and Prof. James Z. Wang. The system was placed online for public use in April 2009. Dr. Dhiraj Joshi (now with the Kodak Research Labs) contributed to an earlier prototype. This is work-in-progress and hence it undergoes algorithmic changes from time to time, in an effort to improve performance. The work is Patent Pending. If you are interested in licensing this technology from Penn State, please contact James Wang.
Because of the limitations on the sources from which Acquine was able to gain some understanding about aesthetics, the opinions expressed by Acquine can be biased by the group of people associated with the sources. Whereas Acquine is possibly less biased than individual people at the time of photo assessment, there is no absolute unbiased opinions on aesthetics.