Multiple Aesthetic Attribute Assessment by Exploiting Relations Among Aesthetic Attributes

Current research of aesthetic assessment for images assumes one aesthetic score or one aesthetic label for an image, ignoring the relations of multiple aesthetic-related attributes. However, most images can be described by multiple aesthetic attributes simultaneously. Therefore, in this paper, we propose multiple aesthetic attribute prediction and classification by modeling relations among aesthetic attributes through Bayesian Networks (BN). In order to realize continuous aesthetic attribute prediction, each aesthetic attribute is represented by a three-node BN, including the discretized aesthetic attribute label, the predicted aesthetic attribute score, and the measurement of the aesthetic attribute score. In addition, the relations among multiple aesthetic attributes are modeled by another discrete BN, whose structure and conditional probabilities are learned from the training data. The attribute measurements are obtained by an existing image-driven regression method. With the learned BN, we infer the true discrete label and continuous score for each attribute by combining the relations among attributes with the previously obtained measurements. Experiments on the Memorability dataset show the superiority of our proposed approach to current image-driven methods for both multiple continuous aesthetic attribute score prediction and multiple discrete aesthetic attribute label classification, indicating the effectiveness of the captured relations for aesthetic quality assessment.