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SaCoseg: Object Cosegmentation by Shape Conformability

机译:SaCoseg:通过形状整合性进行对象细分

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摘要

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.
机译:本文提出了一种基于形状一致性的目标细分方法。与以前的基于图像集中公共对象的区域特征相似度的对象细分方法不同,我们提出的SaCoseg细分算法主要关注图像集中前景对象的形状一致性。在提出的方法中,给定一个图像集,其中隐含的前景对象的外观可能有所变化,但共享相似的形状结构,该图像集中的隐含的通用形状图案可以自动提取,并视为那些未令人满意地分割的图像之前的形状。 SaCoseg算法主要包括四个步骤:1)初始Grabcut分割; 2)通过相干点漂移配准进行形状映射; 3)通过亲和力传播聚类发现共同的形状模式; 4)通过Grabcut在常见形状约束条件下进行细化。为了验证我们提出的算法并为将来的工作建立基准,我们构建了CoShape数据集来评估基于形状的细分。在CoShape数据集上的实验以及与一些相关细分算法的比较证明了所提出的SaCoseg算法的良好性能。

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