  Assessing image aesthetics is a challenging computer vision task. One reason
is that aesthetic preference is highly subjective and may vary significantly
among people for certain images. Thus, it is important to properly model and
quantify such \textit{subjectivity}, but there has not been much effort to
resolve this issue. In this paper, we propose a novel unified probabilistic
framework that can model and quantify subjective aesthetic preference based on
the subjective logic. In this framework, the rating distribution is modeled as
a beta distribution, from which the probabilities of being definitely pleasing,
being definitely unpleasing, and being uncertain can be obtained. We use the
probability of being uncertain to define an intuitive metric of subjectivity.
Furthermore, we present a method to learn deep neural networks for prediction
of image aesthetics, which is shown to be effective in improving the
performance of subjectivity prediction via experiments. We also present an
application scenario where the framework is beneficial for aesthetics-based
image recommendation.
