【SIGGRAPH Asia 2012专题*技术论文】形状和树木：有效的图形联合分析

.

【SIGGRAPH Asia 2012专题*技术论文】
形状和树木：有效的图形联合分析Active Co-Analysis of a Set of Shapes
Yunhai Wang, Shmulik Asafi, Oliver van Kaick, Hao Zhang, Daniel Cohen-Or, Baoquan Chen 摘要：一组形状的无管理联合分析是一个困难的问题，因为单独的形状的几何形状并不总是能完全描述的形状的零件的语义。在本文中，我们提出了一种半监督学习方法，积极协助用户在通过反复提供投入，逐步限制系统的分析。我们引入一个新的约束聚类方法的基础上的弹簧系统中嵌入元素，以更好地尊重它们之间的距离在特征空间中的用户给定的约束。我们还提出了一种主动学习方法，向用户表明他的输入可能是最有效的精炼结果。我们发现，每个单对约束条件的集合影响到许多关系的。因此，该方法仅需要一个稀疏组约束朝向一致的和无差错的语义标记集快速收敛。Abstract：Unsupervised co-analysis of a set of shapes is a dif?cult problem since the geometry of the shapes alone cannot always fully describe the semantics of the shape parts. In this paper, we propose a semi-supervised learning method where the user actively assists in the co-analysis by iteratively providing inputs that progressively constrain the system. We introduce a novel constrained clustering method based on a spring system which embeds elements to better respect their inter-distances in feature space together with the user-given set of constraints. We also present an active learning method that suggests to the user where his input is likely to be the most effective in re?ning the results. We show that each single pair of constraints affects many relations across the set. Thus, the method requires only a sparse set of constraints to quickly converge toward a consistent and error-free semantic labeling of the set.要了解该论文的详情呢，就下载附件好好研究一下哈~更多新技术分享尽在web3D纳金网http://www.narkii.com/