Interactive image segmentation method based on asymmetric quadratic metric
Benting Han, Shuwang zhou · 2023
In this paper, we propose an interactive image segmentation method based on the geodesic voting model. Basically, the segmentation is implemented by thresholding a feature map generated by a geodesic voting strategy. In contrast to the classical geodesic voting method which considers isotropic metric, we exploit state-of-the-art asymmetric quadratic metric for geodesic computation. The main contribution of this work lies at an obstacle-based geodesic computation way, in order to encourage the computed geodesic paths to pass through image features of interest. The use of asymmetric quadratic metric for computing geodesic paths can take advantages from the anisotropy and asymmetry geometric features, thus overcoming the shortcuts problem suffered in the isotropic case. Experiments on real images demonstrate that the proposed method indeed can obtain promising results.