Saliency-based Multiple Region of Interest Detection from a Single 360° image
This addresses the challenge of summarizing large 360° images for applications like virtual reality or surveillance, but it appears incremental as it builds on existing saliency prediction with data augmentation.
The paper tackles the problem of detecting multiple regions of interest from a single 360° image to prevent overlooking important information, proposing a method that uses visual saliency and achieves results validated through subjective evaluation.
360° images are informative -- it contains omnidirectional visual information around the camera. However, the areas that cover a 360° 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° image using the visual saliency as a clue. To deal with the scarce, strongly biased training data of existing single 360° 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° image.