Image cropping is a common way to enhance the aesthetic quality of images. Huge industrial demand and the tediousness of image cropping make automatic image cropping a prosperous task. Existing works, however, face two difficulties: objects are easily truncated and key components of images are discarded by the model. The key to solving this problem is to understand the relations between different components of an image.
These relations break the limit of spatial distance and reflect the contextual information in images, which help the model decide whether to retain a component. Motivated by this, a patch-related graph module is proposed to model the relations between different patches of an image. The patch-related features are extracted by a graph convolution layer and then fused with the original local features by a proposed gated unit.
Moreover, a gradient layer is designed to embed the edge information in the input. The edge-prior input helps the model read the contents of images and reserve the main objects completely. Experimental results show that our model grasps the inter-patch relations well and performs competitively with other state-of-the-art approaches.