FeatureLego: Volume Exploration Using Exhaustive Clustering of Super-Voxels.
FeatureLego: Volume Exploration Using Exhaustive Clustering of Super-Voxels.
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DOI：
10.1109/tvcg.2018.2856744
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发表时间：
2019-09
影响因子：
5.2
通讯作者：
Kaufman A
中科院分区：
文献类型：
--
作者：
Jadhav S;Nadeem S;Kaufman A
我们提出了一个体积的探索框架，mixureLego，它使用了一种新的体素聚类方法，有效地选择语义特征。我们将输入体积划分为一组紧凑的超级体素，这些体素代表最细的选择粒度。然后，我们使用基于图形的聚类方法对这些超体素进行穷举聚类。不同于普遍的蛮力参数采样方法，我们提出了一个有效的算法来执行这种穷举聚类。通过计算一组详尽的聚类，我们的目标是捕捉尽可能多的边界，并确保用户有足够的选择，有效地选择语义相关的功能。此外，我们将所有计算出的集群合并到一棵可用于分层探索的元集群树中。我们实现了一个直观的用户界面，交互式地探索卷使用我们的聚类方法。最后，我们展示了我们的框架在不同模态的多个真实世界数据集上的有效性。
We present a volume exploration framework, FeatureLego, that uses a novel voxel clustering approach for efficient selection of semantic features. We partition the input volume into a set of compact super-voxels that represent the finest selection granularity. We then perform an exhaustive clustering of these super-voxels using a graph-based clustering method. Unlike the prevalent brute-force parameter sampling approaches, we propose an efficient algorithm to perform this exhaustive clustering. By computing an exhaustive set of clusters, we aim to capture as many boundaries as possible and ensure that the user has sufficient options for efficiently selecting semantically relevant features. Furthermore, we merge all the computed clusters into a single tree of meta-clusters that can be used for hierarchical exploration. We implement an intuitive user-interface to interactively explore volumes using our clustering approach. Finally, we show the effectiveness of our framework on multiple real-world datasets of different modalities.