We proposed a contour co-tracking method for co-segmentation of image pairs based on active contour model. Our method comprehensively re-models objects and backgrounds signified by level set functions, and leverages Hellinger distance to measure the similarity between image regions encoded by probability distributions. The main contribution are as follows. 1) The new energy functional, combining a rewarding and a penalty term, relaxes the assumptions of co-segmentation methods. 2) Hellinger distance, fulfilling the triangle inequality, ensures a coherence measurement between probability distributions in metric space, and contributes to finding a unique solution to the energy functional. The proposed contour co-tracking method was carefully verified against five representative methods on four popular datasets, i.e., the images pair dataset (105 pairs), MSRC dataset (30 pairs), iCoseg dataset (66 pairs) and Coseg-rep dataset (25 pairs). The comparison experiments suggest that our method achieves the competitive and even better performance compared to the state-of-the-art co-segmentation methods.

中文翻译：

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一种图像对轮廓协同跟踪方法


我们提出了一种基于主动轮廓模型的图像对联合分割的轮廓联合跟踪方法。我们的方法全面地重新建模由水平集函数表示的对象和背景，并利用海灵格距离来测量由概率分布编码的图像区域之间的相似性。主要贡献如下。 1）新的能量泛函结合了奖励项和惩罚项，放宽了共分割方法的假设。 2）Hellinger距离满足三角不等式，保证了度量空间中概率分布之间的一致性测量，有助于找到能量泛函的唯一解。所提出的轮廓协同跟踪方法在四个流行数据集上针对五种代表性方法进行了仔细验证，即图像对数据集（105对）、MSRC数据集（30对）、iCoseg数据集（66对）和Coseg-rep数据集（25对）对）。比较实验表明，与最先进的共分割方法相比，我们的方法实现了有竞争力甚至更好的性能。