矢量设计的自动分层排列

Matthew Fisher, V. Agarwal, T. Beri
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引用次数: 2

摘要

我们提出了一种将非结构化向量设计转换为对象组的逻辑层次结构的方法。每一组都是一个有意义的集合,由视觉特征(如大小、形状、颜色等)和空间位置相近的对象和模型组成,设计师使用分组原则。我们首先通过部分或完全扁平化输入设计来简化输入设计,并隔离设计中重复的几何形状(例如,由于复制和粘贴操作而重复的图案)。接下来,我们通过将完全封闭在其他对象的几何内部的对象分配为封闭父对象的子对象来构建对象包含层次结构。在最后的聚类阶段,我们使用聚集聚类,通过对所有对对象的视觉和空间特征进行比较和排序,获得自下而上的所有对象的分层分组。空间上的接近会将相距很远的对象分隔开来,但当它们相同(或接近相同)时,设计师通常更喜欢将它们放在一起。为了适应这一点,我们检测几乎相同的对象,并在聚类过程中将它们分组在一起,尽管它们的空间分离。我们进一步限制组的形成,以便设计中的z阶干扰保持视觉外观不受紧密重叠几何的影响。生成的组织等同于原始设计,组织结果用于促进设计中的抽象导航(分层的、横向的或近似的)和选择。我们的技术可以很好地处理各种具有共同可识别对象和结构模式的输入设计。•应用计算→文档分析;•信息系统→集群;
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Hierarchical Arrangement of Vector Designs
We present a method that transforms an unstructured vector design into a logical hierarchy of groups of objects. Each group is a meaningful collection, formed by proximity in visual characteristics (like size, shape, color, etc.) and spatial location of objects and models the grouping principles designers use. We first simplify the input design by partially or completely flattening it and isolate duplicate geometries in the design (for example, repeating patterns due to copy and paste operations). Next we build the object containment hierarchy by assigning objects that are wholly enclosed inside the geometry of other objects as children of the enclosing parent. In the final clustering phase, we use agglomerative clustering to obtain a bottom-up hierarchical grouping of all objects by comparing and ranking all pairs of objects according to visual and spatial characteristics. Spatial proximity segregates far apart objects, but when they are identical (or near identical) designers generally prefer to keep (and edit) them together. To accommodate this, we detect near identical objects and group them together during clustering despite their spatial separation. We further restrict group formation so that z-order disturbances in the design keep the visual appearance unaffected for tightly-overlapping geometry. The generated organization is equivalent to the original design and the organization results are used to facilitate abstract navigation (hierarchical, lateral or near similar) and selections in the design. Our technique works well with a variety of input designs with commonly identifiable objects and structural patterns. CCS Concepts • Applied computing → Document analysis; • Information systems → Clustering;
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