A computer-implemented method comprising: generating a three-dimensional 3D feature map for a digital image using a fully convolutional network FCN, wherein the computer implements the 3D feature map to: identify one or more features of the digital image; and identify an image region for each identified feature, the image region indicating that features' spatial layout within the digital image; generating a region composition graph that includes the identified features and image regions, wherein the computer implements the region composition graph to model one or more mutual dependencies between features of the 3D feature map, the region composition graph including: a plurality of nodes, wherein each node represents a specified region of the digital image; and one or more weighted connecting segments, wherein each weighted connecting segment is weighted according to the strength of mutual dependencies between the features of those nodes that are connected by the connecting segment; performing a graph convolution on the region composition graph to determine a feature aesthetic value for each node according to the weightings in the node's weighted connecting segments; wherein a graph convolution performs message passing across the region composition graph to model the one or more mutual dependencies; and calculating a weighted average for each node's feature aesthetic value, such that a combined weighted average over the nodes of the digital image represents a combined level of aesthetic appeal for the digital image. generating a three-dimensional 3D feature map for a digital image using a fully convolutional network FCN, wherein the computer implements the 3D feature map to: identify one or more features of the digital image; and identify an image region for each identified feature, the image region indicating that features' spatial layout within the digital image; generating a region composition graph that includes the identified features and image regions, wherein the computer implements the region composition graph to model one or more mutual dependencies between features of the 3D feature map, the region composition graph including: a plurality of nodes, wherein each node represents a specified region of the digital image; and one or more weighted connecting segments, wherein each weighted connecting segment is weighted according to the strength of mutual dependencies between the features of those nodes that are connected by the connecting segment; identify one or more features of the digital image; and identify an image region for each identified feature, the image region indicating that features' spatial layout within the digital image; generating a region composition graph that includes the identified features and image regions, wherein the computer implements the region composition graph to model one or more mutual dependencies between features of the 3D feature map, the region composition graph including: a plurality of nodes, wherein each node represents a specified region of the digital image; and one or more weighted connecting segments, wherein each weighted connecting segment is weighted according to the strength of mutual dependencies between the features of those nodes that are connected by the connecting segment; a plurality of nodes, wherein each node represents a specified region of the digital image; and one or more weighted connecting segments, wherein each weighted connecting segment is weighted according to the strength of mutual dependencies between the features of those nodes that are connected by the connecting segment; performing a graph convolution on the region composition graph to determine a feature aesthetic value for each node according to the weightings in the node's weighted connecting segments; wherein a graph convolution performs message passing across the region composition graph to model the one or more mutual dependencies; and calculating a weighted average for each node's feature aesthetic value, such that a combined weighted average over the nodes of the digital image represents a combined level of aesthetic appeal for the digital image.