Intelligent Visualization Interfaces

可视化已经成为科学研究和应用的各个领域中越来越重要的工具。 它对于各种任务至关重要，例如评估工程设计，理解大规模模拟，查看多模态3D医疗数据，挖掘庞大的Web数据库以及分析国土安全信息。 然而，可视化并没有在许多领域的日常数据分析中得到广泛的使用，因为尽管可视化系统软件和硬件正在不断进步，但用于这些任务的适当用户界面的开发却落后了。 在现实世界的应用中，经常会遇到数据集，除了具有时空性质外，还具有大量的属性（高维）。 为低维数据分析而开发的传统用户界面的复杂性随着数据的维度增加而快速增长，使得许多用户不能或不愿意执行在高维空间中创建信息可视化所需的耗时且繁琐的步骤。 在这个项目中，PI将通过将机器学习纳入科学家用于探索和与数据交互的界面来应对这一挑战，以开发针对高维数据可视化任务的智能界面，并显着提高可用性。 在一项初步研究中，PI展示了他的方法在执行传统方法无法很好完成的具有挑战性的体积分类任务方面的潜在力量，通过将机器学习与绘画隐喻相结合，在分类过程中实现更直观的用户意图规范。 用户直接在体积渲染图像或体积的选定横截面上交互地绘制，并且通过将一种颜色的绘制应用于表示感兴趣材料的体积的部分并且将另一种颜色的绘制应用于不期望的区域，来给予对要分类的材料的完全控制。 系统使用绘制的区域（体积的非常小的子集）作为训练数据来学习如何对整个体积进行分类，将每个体素映射到指示体素是感兴趣材料的一部分的可能性的值;然后将这种不确定性映射到直接体积渲染的不透明度。 虽然原型系统采用了确定性人工神经网络，但在本项目中，PI将使用具有不同特征的数据集来探索支持向量机或贝叶斯方法是否可以提供上级性能和更好的可扩展性，即计算要求更低，同时提供更好的交互性。 更广泛的影响：PI在本项目中开发的新的可视化界面技术将大大提高可视化系统的可用性和效率，并扩大用户群，使其能够成功应对复杂的可视化和分析任务所带来的日益增长的挑战。 通过将有效的机器学习纳入数据可视化和交互过程，PI将使用户从重复的任务和当今系统的复杂界面中解放出来。 PI方法的核心概念，如“学习分类”和“学习跟踪”是非常强大的，并将建议科学家重新思考如何进行数据分析和可视化，以及使他们能够“重用”和“共享”宝贵的可视化经验。

Visualization has become an increasingly important tool in all areas of scientific research and applications. It is crucial to diverse tasks such as evaluation of engineering designs, the understanding of large-scale simulations, the viewing of multi-modal 3D medical data, the mining of enormous web databases, and the analysis of homeland security information. However, visualization has not gained widespread use for routine data analysis in many fields, because while continuing advances are being made in visualization system software and hardware, the development of appropriate user interfaces for these tasks has lagged behind. In real-world applications, it is common to encounter data sets, which in addition to being of a spatial-temporal nature possess a large number of attributes (high dimensionality). The complexity of traditional user interfaces that were developed for low-dimensional data analysis grows rapidly as the dimensionality of the data increases, so that many users are unable or unwilling to perform the time-consuming and tedious steps required to create an informative visualization in a high dimensional space. In this project, the PI will address this challenge by incorporating machine learning into the interfaces used by scientists to explore and interact with their data, to develop intelligent interfaces targeting high-dimensional data visualization tasks with significantly improved usability. In a preliminary study the PI demonstrated the potential power of his approach in performing challenging volume classification tasks that conventional methods failed to do well, by coupling machine learning with a painting metaphor to enable more intuitive specification of user intent in the classification process. Users interactively paint directly on the volume rendered images or selected cross sections of the volume, and are given full control of what materials to classify by applying paint of one color to parts of the volume representing materials of interest and paint of another color to regions that are not desired. The system uses the painted regions (a very small subset of the volume) as training data to learn how to classify the whole volume, mapping each voxel into a value indicating the likelihood that the voxel is part of the material of interest; this uncertainty is then mapped to opacity for direct volume rendering. While the prototype system employed a deterministic artificial neural network, in this project the PI will use data sets with varying characteristics to explore whether support vector machines or Bayesian approaches can provide superior performance and better scalability in terms of being less computationally demanding while affording better interactivity. Broader Impacts: The new visual interface technology to be developed by the PI in this project will dramatically increase the usability and efficiency of visualization systems and broaden the base of users who can successfully tackle the increasing challenges presented by complex visualization and analysis tasks. By incorporating effective machine learning into the process of data visualization and interaction, the PI will free users from repetitive tasks and the complex interfaces of today's systems. Concepts such as "learning to classify" and "learning to track" which are central to the PI's approach are very powerful, and will suggest to scientists that they rethink about how data analysis and visualization can be done, as well as enabling them to "reuse" and "share" valuable visualization experiences.