SwipeCut: Interactive Segmentation with Diversified Seed Proposals
This work addresses the problem of user-friendly interactive segmentation for users on small touchscreen devices, presenting an incremental improvement over existing methods.
The paper tackles the cumbersome requirement of precise annotations for interactive image segmentation on small touchscreen devices by proposing an efficient seed proposal method that actively suggests annotation seeds, allowing users to simply swipe through relevant seeds. The algorithm achieves high segmentation accuracy with short response times and less user feedback, as evaluated on six publicly available datasets.
Interactive image segmentation algorithms rely on the user to provide annotations as the guidance. When the task of interactive segmentation is performed on a small touchscreen device, the requirement of providing precise annotations could be cumbersome to the user. We design an efficient seed proposal method that actively proposes annotation seeds for the user to label. The user only needs to check which ones of the query seeds are inside the region of interest (ROI). We enforce the sparsity and diversity criteria on the selection of the query seeds. At each round of interaction the user is only presented with a small number of informative query seeds that are far apart from each other. As a result, we are able to derive a user friendly interaction mechanism for annotation on small touchscreen devices. The user merely has to swipe through on the ROI-relevant query seeds, which should be easy since those gestures are commonly used on a touchscreen. The performance of our algorithm is evaluated on six publicly available datasets. The evaluation results show that our algorithm achieves high segmentation accuracy, with short response time and less user feedback.