  360{\deg} images are informative -- it contains omnidirectional visual
information around the camera. However, the areas that cover a 360{\deg} image
is much larger than the human's field of view, therefore important information
in different view directions is easily overlooked. To tackle this issue, we
propose a method for predicting the optimal set of Region of Interest (RoI)
from a single 360{\deg} image using the visual saliency as a clue. To deal with
the scarce, strongly biased training data of existing single 360{\deg} image
saliency prediction dataset, we also propose a data augmentation method based
on the spherical random data rotation. From the predicted saliency map and
redundant candidate regions, we obtain the optimal set of RoIs considering both
the saliency within a region and the Interaction-Over-Union (IoU) between
regions. We conduct the subjective evaluation to show that the proposed method
can select regions that properly summarize the input 360{\deg} image.
