ACTIVE LEARNING GUIDED INTERACTIONS FOR CONSISTENT IMAGE SEGMENTATION WITH REDUCED USER INTERACTIONS

Interactive techniques leverage the expert knowledge of users to produce accurate image segmentations. However, the segmentation accuracy varies with the users. Additionally, users may also require training with the algorithm and its exposed parameters to obtain the best segmentation with minimal effort. Our work combines active learning with interactive segmentation and (i) achieves as good accuracy compared to a fully user guided segmentation but with significantly lower number of user interactions (on average 50%), and (ii) achieves robust segmentation by reducing segmantation variability with user inputs. Our approach interacts with user to suggest gestures or seed point placements. We present extensive experimental evaluation of our results on two different publicly available datasets.