IEEE Trans Image Process. 2015 Mar;24(3):943-55. doi: 10.1109/TIP.2014.2387384. Epub 2015 Jan 5.
In this paper, an object cosegmentation method based on shape conformability is proposed. Different from the previous object cosegmentation methods which are based on the region feature similarity of the common objects in image set, our proposed SaCoseg cosegmentation algorithm focuses on the shape consistency of the foreground objects in image set. In the proposed method, given an image set where the implied foreground objects may be varied in appearance but share similar shape structures, the implied common shape pattern in the image set can be automatically mined and regarded as the shape prior of those unsatisfactorily segmented images. The SaCoseg algorithm mainly consists of four steps: 1) the initial Grabcut segmentation; 2) the shape mapping by coherent point drift registration; 3) the common shape pattern discovery by affinity propagation clustering; and 4) the refinement by Grabcut with common shape constraint. To testify our proposed algorithm and establish a benchmark for future work, we built the CoShape data set to evaluate the shape-based cosegmentation. The experiments on CoShape data set and the comparison with some related cosegmentation algorithms demonstrate the good performance of the proposed SaCoseg algorithm.
本文提出了一种基于形状一致性的目标协同分割方法。与之前基于图像集中共有目标区域特征相似性的目标协同分割方法不同,我们提出的 SaCoseg 协同分割算法关注的是图像集中前景目标的形状一致性。在该方法中,给定一个图像集,其中隐含的前景对象的外观可能会有所不同,但共享相似的形状结构,图像集中隐含的公共形状模式可以自动挖掘出来,并作为那些分割不理想的图像的形状先验。SaCoseg 算法主要包括四个步骤:1)初始 Grabcut 分割;2)通过一致点漂移配准进行形状映射;3)通过亲和传播聚类发现公共形状模式;4)使用具有公共形状约束的 Grabcut 进行细化。为了验证我们提出的算法并为未来的工作建立一个基准,我们构建了 CoShape 数据集来评估基于形状的协同分割。在 CoShape 数据集上的实验以及与一些相关协同分割算法的比较表明,所提出的 SaCoseg 算法具有良好的性能。