Subspace based active contours with a joint distribution metric for semi-supervised natural image segmentation

In this paper, we present an efficient active contour with a joint distribution metric for semi-supervised natural image segmentation. Firstly, we project an RGB image into two-dimensional subspace and draw a polygon curve around the Region of Interest (ROI) as the initial evolving curve. Then, we model the regional statistics in terms of joint probability distributions and propose an effective distribution metric to regularize the active contours for evolution. Subsequently, we convert the resultant zero level set function into binary pattern and find all the 8-connected regions. Finally, the largest region is selected as the desired ROI and smoothed with a circular averaging filter so that the corresponding final segmentation result can be obtained. Meanwhile, the proposed approach also features fast convergence and easy implementation in comparison with the traditional methods, which need a laborious process of re-initializing the zero level set in terms of a sign distance function (SDF) periodically. The experiments show the promising results.