Date of Original Version



Conference Proceeding

Abstract or Description

Computational aesthetics is the study of applying machine learning techniques to identify aesthetically pleasing imagery. Prior work used online datasets scraped from large user communities like Flikr to get labeled data. However, online imagery represents results late in the media generation process, as the photographer has already framed the shot and then picked the best results to upload. Thus, this technique can only identify quality imagery once it has been taken. In contrast, automatically creating pleasing imagery requires understanding the imagery present earlier in the process. This paper applies computational aesthetics techniques to a novel dataset from earlier in that process in order to understand how the problem changes when an autonomous agent, like a robot or a real-time camera aid, creates pleasing imagery instead of simply identifying it.



Included in

Robotics Commons



Published In

Proceedings of International Conference on Pattern Recognition (ICPR), 2452-3455.