Exploring Metrics to Establish an Optimal Model for Image Aesthetic Assessment and Analysis

To establish an optimal model for photo aesthetic assessment, in this paper, an internal metric called the disentanglement-measure (D-measure) is introduced, which reflects the disentanglement degree of the final layer FC (full connection) nodes of convolutional neural network (CNN). By combining the F-measure with the D-measure to obtain an FD measure, an algorithm of determining the optimal model from many photo score prediction models generated by CNN-based repetitively self-revised learning (RSRL) is proposed. Furthermore, the aesthetics features of the model regarding the first fixation perspective (FFP) and the assessment interest region (AIR) are defined by means of the feature maps so as to analyze the consistency with human aesthetics. The experimental results show that the proposed method is helpful in improving the efficiency of determining the optimal model. Moreover, extracting the FFP and AIR of the models to the image is useful in understanding the internal properties of these models related to the human aesthetics and validating the external performances of the aesthetic assessment.