Designers need to consider not only perceptual effectiveness but also visual styles when creating an infographic. This process can be difficult and time consuming for professional designers, not to mention non-expert users, leading to the demand for automated infographics design. As a first step, we focus on timeline infographics, which have been widely used for centuries. We contribute an end-to-end approach that automatically extracts an extensible timeline template from a bitmap image. Our approach adopts a deconstruction and reconstruction paradigm. At the deconstruction stage, we propose a multi-task deep neural network that simultaneously parses two kinds of information from a bitmap timeline: 1) the global information, i.e., the representation, scale, layout, and orientation of the timeline, and 2) the local information, i.e., the location, category, and pixels of each visual element on the timeline. At the reconstruction stage, we propose a pipeline with three techniques, i.e., Non-Maximum Merging, Redundancy Recover, and DL GrabCut, to extract an extensible template from the infographic, by utilizing the deconstruction results. To evaluate the effectiveness of our approach, we synthesize a timeline dataset (4296 images) and collect a real-world timeline dataset (393 images) from the Internet. We first report quantitative evaluation results of our approach over the two datasets. Then, we present examples of automatically extracted templates and timelines automatically generated based on these templates to qualitatively demonstrate the performance. The results confirm that our approach can effectively extract extensible templates from real-world timeline infographics.

翻译：设计师在创作信息图时，不仅需要考虑感知有效性，还需兼顾视觉风格。这一过程对于专业设计师而言既困难又耗时，更不用说非专业用户了，因此产生了对自动化信息图设计的需求。作为第一步，我们聚焦于已广泛使用数百年的时间轴信息图。我们提出了一种端到端方法，可从位图图像中自动提取可扩展的时间轴模板。该方法采用了解构与重构的范式。在解构阶段，我们设计了一个多任务深度神经网络，能同时解析位图时间轴中的两类信息：1）全局信息，即时间轴的表征、比例、布局和方向；2）局部信息，即时间轴上每个视觉元素的位置、类别和像素。在重构阶段，我们提出了一种包含三种技术的流水线——非极大值合并、冗余恢复和深度学习GrabCut——通过利用解构结果从信息图中提取可扩展模板。为评估方法的有效性，我们合成了一份时间轴数据集（4296张图像），并从互联网收集了一份真实世界时间轴数据集（393张图像）。我们首先报告了该方法在两个数据集上的定量评估结果，随后展示了自动提取模板的示例及基于这些模板自动生成的时间轴，以定性说明性能。结果证实，我们的方法能有效从真实世界的时间轴信息图中提取可扩展模板。