Photo aesthetic quality assessment via label distribution learning

Abstract

Automatic prediction of photo aesthetic quality is useful for many practical purposes. Current computational approaches typically solved this problem by assigning a categorical label (good or bad) to a photo. However, due to the subjectivity and complexity of humans aesthetic judgments, only a categorical label is insufficient to represent humans perceived aesthetic quality of a photo. This paper focuses on an interesting problem: is it possible to predict the crowed opinions about the aesthetic quality of a photo? The crowed opinion here is expressed by the distribution of scores given by a number of subjects. For each given photo, a deep convolutional neural network (DCNN) is utilized to calculate its feature representation. Afterwards, the crowed opinion prediction problem is formulated as one of label distribution learning (LDL). Experiments show that the proposed method is highly effective and outperforms state-of-the-art algorithms.