  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.
