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Motion-Coherent Affinities for Hypergraph Based Motion Segmentation

机译:基于超图运动分割的运动相干亲和力

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Motion segmentation is the task of classifying the feature trajectories in an image sequence to different motions. Hypergraph based approaches use a specific graph to incorporate higher order similarities for the estimation of motion clusters. They follow the concept of hypothesis generation and validation. For the sampling of hypotheses, a high probability of selecting clean samples, i.e. samples consisting of points from the same cluster, is desired. Many approaches use spatial proximity to build an auxiliary graph for the sampling. But, spatial proximity is often not sufficient to capture the main affinities for motion segmentation. Thus, we introduce a simple but effective model for incorporating motion-coherent affinities into the auxiliary graph. The evaluation on two state of the art benchmarks shows that the hypotheses generated from the resulting hypergraphs lead to a significant decrease of the segmentation error. Additionally, less computation time is required due to a reduced hypergraph complexity.
机译:运动分割是将图像序列中的特征轨迹分类到不同运动的任务。基于超图的方法使用特定的图形来包含高阶相似性,以估计运动群集。他们遵循假设生成和验证的概念。对于假设的抽样,期望选择清洁样品的高概率,即由来自同一簇的点组成的样本。许多方法使用空间邻近来构建用于采样的辅助图。但是,空间接近通常不足以捕获运动分割的主要亲和力。因此,我们介绍了一种简单但有效的模型,用于将运动相干的亲和力掺入辅助图中。对艺术基准的两个状态的评估表明,从所产生的超图生成的假设导致分割误差的显着降低。另外,由于减少的超图复杂度,需要较少的计算时间。

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