Neutro-Connectedness Cut
This work addresses the problem of improving efficiency and robustness in interactive image segmentation for computer vision applications, representing an incremental advancement.
The paper tackles the challenges in interactive image segmentation by proposing Neutro-Connectedness Cut (NC-Cut), a hybrid method that combines pixel-wise appearance and region-based topology to reduce sensitivity to initial inputs and minimize user interactions, achieving superior performance over state-of-the-art methods on 265 images.
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).