  Series photo selection (SPS) is an important branch of the image aesthetics
quality assessment, which focuses on finding the best one from a series of
nearly identical photos. While a great progress has been observed, most of the
existing SPS approaches concentrate solely on extracting features from the
original image, neglecting that multiple views, e.g, saturation level, color
histogram and depth of field of the image, will be of benefit to successfully
reflecting the subtle aesthetic changes. Taken multi-view into consideration,
we leverage a graph neural network to construct the relationships between
multi-view features. Besides, multiple views are aggregated with an
adaptive-weight self-attention module to verify the significance of each view.
Finally, a siamese network is proposed to select the best one from a series of
nearly identical photos. Experimental results demonstrate that our model
accomplish the highest success rates compared with competitive methods.
