  Automatic Image Cropping is a challenging task with many practical downstream
applications. The task is often divided into sub-problems - generating cropping
candidates, finding the visually important regions, and determining aesthetics
to select the most appealing candidate. Prior approaches model one or more of
these sub-problems separately, and often combine them sequentially. We propose
a novel convolutional neural network (CNN) based method to crop images
directly, without explicitly modeling image aesthetics, evaluating multiple
crop candidates, or detecting visually salient regions. Our model is trained on
a large dataset of images cropped by experienced editors and can simultaneously
predict bounding boxes for multiple fixed aspect ratios. We consider the aspect
ratio of the cropped image to be a critical factor that influences aesthetics.
Prior approaches for automatic image cropping, did not enforce the aspect ratio
of the outputs, likely due to a lack of datasets for this task. We, therefore,
benchmark our method on public datasets for two related tasks - first,
aesthetic image cropping without regard to aspect ratio, and second, thumbnail
generation that requires fixed aspect ratio outputs, but where aesthetics are
not crucial. We show that our strategy is competitive with or performs better
than existing methods in both these tasks. Furthermore, our one-stage model is
easier to train and significantly faster than existing two-stage or end-to-end
methods for inference. We present a qualitative evaluation study, and find that
our model is able to generalize to diverse images from unseen datasets and
often retains compositional properties of the original images after cropping.
Our results demonstrate that explicitly modeling image aesthetics or visual
attention regions is not necessarily required to build a competitive image
cropping algorithm.
