# Camera View Adjustment Prediction for Improving Image Composition

arXiv: https://arxiv.org/abs/2104.07608 · HTML: https://mlsanity.com/paper/2104.07608/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Aesthetic-Driven Image Enhancement by Adversarial Learning](https://mlsanity.com/paper/1707.05251/) (2017)
- [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)
- [Pano2Vid: Automatic Cinematography for Watching 360circ Videos](https://mlsanity.com/paper/1612.02335/) (2016)
- [Image Cropping with Composition and Saliency Aware Aesthetic Score Map](https://mlsanity.com/paper/1911.10492/) (2019)
- [Deep Cropping via Attention Box Prediction and Aesthetics Assessment](https://mlsanity.com/paper/1710.08014/) (2017)
- [Listwise View Ranking for Image Cropping](https://mlsanity.com/paper/1905.05352/) (2019)
- [Creatism: A deep-learning photographer capable of creating professional work](https://mlsanity.com/paper/1707.03491/) (2017)
- [An End-to-End Neural Network for Image Cropping by Learning Composition from Aesthetic Photos](https://mlsanity.com/paper/1907.01432/) (2019)

## Built on by (most cited first, 4 of 4)

- [Human-centric Image Cropping with Partition-aware and Content-preserving Features](https://mlsanity.com/paper/2207.10269/) (2022)
- [Predicting Scores of Various Aesthetic Attribute Sets by Learning from Overall Score Labels](https://mlsanity.com/paper/2312.03222/) (2023)
- [Aesthetic Camera Viewpoint Suggestion with 3D Aesthetic Field](https://mlsanity.com/paper/2602.20363/) (2026)
- [Considering user agreement in learning to predict the aesthetic quality](https://mlsanity.com/paper/2110.06956/) (2021)

## Abstract (arXiv)

Image composition plays an important role in the quality of a photo. However, not every camera user possesses the knowledge and expertise required for capturing well-composed photos. While post-capture cropping can improve the composition sometimes, it does not work in many common scenarios in which the photographer needs to adjust the camera view to capture the best shot. To address this issue, we propose a deep learning-based approach that provides suggestions to the photographer on how to adjust the camera view before capturing. By optimizing the composition before a photo is captured, our system helps photographers to capture better photos. As there is no publicly-available dataset for this task, we create a view adjustment dataset by repurposing existing image cropping datasets. Furthermore, we propose a two-stage semi-supervised approach that utilizes both labeled and unlabeled images for training a view adjustment model. Experiment results show that the proposed semi-supervised approach outperforms the corresponding supervised alternatives, and our user study results show that the suggested view adjustment improves image composition 79% of the time.