  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.
