Exploring CNN-based models for image's aesthetic score prediction with using ensemble
This is an incremental improvement for automated image quality assessment, primarily benefiting computer vision applications.
The authors tackled image aesthetic score prediction by proposing an ensemble of CNN-based models, which improved performance, and they analyzed attention regions to check consistency with image subjects.
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.