Neutro-Connectedness Cut.
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- Record sourced from PubMed, PMID 27479963.
- Also identified by DOI 10.1109/TIP.2016.2594485.
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Abstract
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 region of interest (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 paper, 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, NC Cut (NC-Cut), which can overcome the above two problems by utilizing both pixelwise appearance information and region-based NC properties. We evaluate the proposed NC-Cut by employing two image data sets (265 images), and demonstrate that the proposed approach outperforms the state-of-the-art interactive image segmentation methods (Grabcut, MILCut, One-Cut, MGC<sub>max</sub><sup>sum</sup>, and pPBC).