Algorithmic inferencing of aesthetics and emotion in natural images: An exposition

Abstract

Initial studies have shown that automatic inference of high-level image quality or aesthetics is very challenging. The ability to do so, however, can prove beneficial in many applications. In this paper, we define the aesthetics gap and discuss key aspects of the problem of aesthetics and emotion inference in natural images. We introduce precise, relevant questions to be answered, the effect that the target audience has on the problem specification, broad technical solution approaches, and assessment criteria. We then report on our effort to build real-world datasets that provide viable approaches to test and compare algorithms for these problems, presenting statistical analysis of and insights into them.