Image Cropping with Content and Composition Attribute-aware Global Relation Reasoning

Image cropping aims to find visually pleasing content in an image, which will enhance its aesthetic quality. Existing image cropping approaches mainly emphasize the geometric properties of images, such as composition and layout, neglecting the rich aesthetic information available from the physical attributes (e.g., content and themes), and background information beyond the foreground in images. Consequently, this article proposes an image cropping method based on the content and composition attribute-aware global relation reasoning, which aims at guiding the generation of cropped sub-images by exploring critical attributes based on content and composition as well as global object correlations that affect aesthetics in images. Particularly, to comprehensively introduce aesthetic information into image cropping, we capture feature representations reinforced by content and composition attributes simultaneously. The feature representations can strengthen the visual aesthetics of cropped sub-images. To make the cropped sub-images amply contain more global information, we introduce a global relation reasoning branch in the proposed cropping module, which can fully exploit the dependency relationship between the foreground and background in images. Extensive experiments on image cropping benchmarks demonstrate that our approach is superior to state-of-the-art image cropping methods.