Exploring CNN-based models for image's aesthetic score prediction with using ensemble

Abstract

In this paper, we proposed a framework of constructing two types of the automatic image aesthetics assessment models with different CNN architectures and improving the performance of the image's aesthetic score prediction by the ensemble. Moreover, the attention regions of the models to the images are extracted to analyze the consistency with the subjects in the images. The experimental results verify that the proposed method is effective for improving the AS prediction. Moreover, it is found that the AS classification models trained on XiheAA dataset seem to learn the latent photography principles, although it can't be said that they learn the aesthetic sense.

References in corpus (6)

• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• Ensemble deep learning: A review
• Image Aesthetic Assessment: An Experimental Survey
• Exploring to establish an appropriate model for image aesthetic assessment via CNN-based RSRL: An empirical study
• Sample-specific repetitive learning for photo aesthetic assessment and highlight region extraction
• Assessing aesthetics of generated abstract images using correlation structure