Active Learning Guided User Interactions for Consistent Image Segmentation

Interactive techniques leverage the expert knowledge of users to produce accurate image segmentations. However, the segmentation accuracy varies with the users. Furthermore, users require some training with the algorithm and its exposed parameters to obtain the best segmentation with minimal effort. Our work combines active learning with interactive segmentation to (i) achieve the same accuracy as an interactive segmentation alone with significantly lower number of user interactions (on average 50%), and (ii) improves the consistency of segmentation with variable user inputs by iteratively suggesting gestures for labelling to the user. We present extensive experimental evaluation of our results on two different publicly available datasets.