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The task of aesthetic quality assessment is complicated due to itssubjectivity. In recent years, the target representation of image aestheticquality has changed from a one-dimensional binary classification label ornumerical score to a multi-dimensional score distribution. According to currentmethods, the ground truth score distributions are straightforwardly regressed.However, the subjectivity of aesthetics is not taken into account, that is tosay, the psychological processes of human beings are not taken intoconsideration, which limits the performance of the task. In this paper, wepropose a Deep Drift-Diffusion (DDD) model inspired by psychologists to predictaesthetic score distribution from images. The DDD model can describe thepsychological process of aesthetic perception instead of traditional modelingof the results of assessment. We use deep convolution neural networks toregress the parameters of the drift-diffusion model. The experimental resultsin large scale aesthetic image datasets reveal that our novel DDD model issimple but efficient, which outperforms the state-of-the-art methods inaesthetic score distribution prediction. Besides, different psychologicalprocesses can also be predicted by our model.

A Deep Drift-Diffusion Model for Image Aesthetic Score Distribution Prediction