  Interactive image segmentation is a challenging task and receives increasing
attention recently; however, two major drawbacks exist in interactive
segmentation approaches. First, the segmentation performance of ROI-based
methods is sensitive to the initial ROI: different ROIs may produce results
with great difference. Second, most seed-based methods need intense
interactions, and are not applicable in many cases. In this work, we generalize
the Neutro-Connectedness (NC) to be independent of top-down priors of objects
and to model image topology with indeterminacy measurement on image regions,
propose a novel method for determining object and background regions, which is
applied to exclude isolated background regions and enforce label consistency,
and put forward a hybrid interactive segmentation method, Neutro-Connectedness
Cut (NC-Cut), which can overcome the above two problems by utilizing both
pixel-wise appearance information and region-based NC properties. We evaluate
the proposed NC-Cut by employing two image datasets (265 images), and
demonstrate that the proposed approach outperforms state-of-the-art interactive
image segmentation methods (Grabcut, MILCut, One-Cut, MGC_max^sum and pPBC).
