基于协同热扩散模型的协同图像分割算法

Heat co-diffusion based image co-segmentation algorithm

针对传统的图像分割算法由于缺乏先验知识得不到理想的分割结果,或需要大量的人工交互问题,本文提出了一种基于协同热扩散模型的协同图像分割算法。算法通过建立图像集合中的扩散模型,利用传导边将图像连接成一个统一的扩散网络,从而将图像集合的协同分割问题转化为3D扩散模型最大增益的求解问题,最终根据子模优化理论对其进行求解。在协同分割数据集上的大量对比实验,验证了本文协同分割算法的优异性能。

The traditional image segmentation algorithms can be roughly divided into two types,i.e.,unsupervised bottom-up segmentation algorithms and supervised segmentation algorithms with interactions.For complex scenes or complicated targets,the former usually fails to work well due to the lack of prior knowledge.And the latter can achieve satisfactory segmentations with users＇ interactions,but it greatly increases the burden of the users.Recently,co-segmentation as a weak-supervised algorithm has got more and more attention.In this paper,we propose a heat co-diffusion based image co-segmentation algorithm.We first establish a 3Dco-diffusion network between images by connecting conduction edge with similar objects.Then,the image co-segmentation is converted into how to get the maximal marginal gain in the conducting network.It is proved that the problem could be solved by sub-modular theory.Compared with several state of the art co-segmentation methods,the experimental results of the proposed method show good performance on benchmark datasets.