# Image Cropping under Design Constraints

arXiv: https://arxiv.org/abs/2310.08892 · HTML: https://mlsanity.com/paper/2310.08892/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 13 Oct 2023.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [TranSalNet: Towards perceptually relevant visual saliency prediction](https://mlsanity.com/paper/2110.03593/) (2021)
- [Reliable and Efficient Image Cropping: A Grid Anchor based Approach](https://mlsanity.com/paper/1904.04441/) (2019)
- [Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative Study](https://mlsanity.com/paper/1701.01480/) (2017)
- [Grid Anchor based Image Cropping: A New Benchmark and An Efficient Model](https://mlsanity.com/paper/1909.08989/) (2019)
- [Learning to Compose with Professional Photographs on the Web](https://mlsanity.com/paper/1702.00503/) (2017)
- [Deep Cropping via Attention Box Prediction and Aesthetics Assessment](https://mlsanity.com/paper/1710.08014/) (2017)
- [Human-centric Image Cropping with Partition-aware and Content-preserving Features](https://mlsanity.com/paper/2207.10269/) (2022)
- [ClipCrop: Conditioned Cropping Driven by Vision-Language Model](https://mlsanity.com/paper/2211.11492/) (2022)
- [Fast-AT: Fast Automatic Thumbnail Generation using Deep Neural Networks](https://mlsanity.com/paper/1612.04811/) (2016)
- [Repurposing Existing Deep Networks for Caption and Aesthetic-Guided Image Cropping](https://mlsanity.com/paper/2201.02280/) (2022)

## Built on by (most cited first, 1 of 1)

- [ShotCrop3: Cropping Human-Centric Images into Cinematic Triple-Shot Compositions](https://mlsanity.com/paper/2606.05635/) (2026)

## Abstract (arXiv)

Image cropping is essential in image editing for obtaining a compositionally enhanced image. In display media, image cropping is a prospective technique for automatically creating media content. However, image cropping for media contents is often required to satisfy various constraints, such as an aspect ratio and blank regions for placing texts or objects. We call this problem image cropping under design constraints. To achieve image cropping under design constraints, we propose a score function-based approach, which computes scores for cropped results whether aesthetically plausible and satisfies design constraints. We explore two derived approaches, a proposal-based approach, and a heatmap-based approach, and we construct a dataset for evaluating the performance of the proposed approaches on image cropping under design constraints. In experiments, we demonstrate that the proposed approaches outperform a baseline, and we observe that the proposal-based approach is better than the heatmap-based approach under the same computation cost, but the heatmap-based approach leads to better scores by increasing computation cost. The experimental results indicate that balancing aesthetically plausible regions and satisfying design constraints is not a trivial problem and requires sensitive balance, and both proposed approaches are reasonable alternatives.