Grid Anchor based Image Cropping: A New Benchmark and An Efficient Model

Hui Zeng, Lida Li, Zisheng Cao, Lei Zhang (Corresponding Author)

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)


Image cropping aims to improve the composition as well as aesthetic quality of an image by removing extraneous content from it. Most of the existing image cropping databases provide only one or several human-annotated bounding boxes as the groundtruths, which can hardly reflect the non-uniqueness and flexibility of image cropping in practice. The employed evaluation metrics such as intersection-over-union cannot reliably reflect the real performance of a cropping model, either. This work revisits the problem of image cropping, and presents a grid anchor based formulation by considering the special properties and requirements (e.g., local redundancy, content preservation, aspect ratio) of image cropping. Our formulation reduces the searching space of candidate crops from millions to no more than ninety. Consequently, a grid anchor based cropping benchmark is constructed, where all crops of each image are annotated and more reliable evaluation metrics are defined. To meet the practical demands of robust performance and high efficiency, we also design an effective and lightweight cropping model. By simultaneously considering the region of interest and region of discard, and leveraging multi-scale information, our model can robustly output visually pleasing crops for images of different scenes. With less than 2.5M parameters, our model runs at a speed of 200 FPS on one single GTX 1080Ti GPU and 12 FPS on one i7-6800K CPU. The code is available at:

Original languageEnglish
Pages (from-to)1304-1319
Number of pages16
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number3
Publication statusPublished - 1 Mar 2022


  • Image cropping
  • deep learning
  • image aesthetics
  • photo cropping

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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