  Image composition assessment is crucial in aesthetic assessment, which aims
to assess the overall composition quality of a given image. However, to the
best of our knowledge, there is neither dataset nor method specifically
proposed for this task. In this paper, we contribute the first composition
assessment dataset CADB with composition scores for each image provided by
multiple professional raters. Besides, we propose a composition assessment
network SAMP-Net with a novel Saliency-Augmented Multi-pattern Pooling (SAMP)
module, which analyses visual layout from the perspectives of multiple
composition patterns. We also leverage composition-relevant attributes to
further boost the performance, and extend Earth Mover's Distance (EMD) loss to
weighted EMD loss to eliminate the content bias. The experimental results show
that our SAMP-Net can perform more favorably than previous aesthetic assessment
approaches.
