SAMP-Viz: An Interactive Multivariable Volume Visualization Framework Based on Subspace Analysis and Multidimensional Projection

Abstract

Volume rendering is an important technique of scientific visualization that can help people analyze and understand multivariable volume data effectively. Since, the previous visualization methods of multivariable volume data are not intuitive and difficult to operate, we propose a novel framework of visualizing multivariable volume data, which combines subspace clustering with radial coordinate visualization (RadViz) from the global pattern analysis to the local feature exploration. Since multivariable data generally have a large data size, the feature sampling is performed to extract some representative points. In order to explore the features interactively, the sample points extracted from high-dimensional space are projected into a low-dimensional space. Through selecting different sample points interactively, users can switch and explore different subspaces in real-time. For the further analysis of the local details in the selected subspace, we utilize the RadViz technique to present the data patterns in the subspace. Thus, the relationships of the data among different dimensions can be recognized intuitively. The result of the experiment shows that our method can help users explore the complex features in volume data deeply and express the data patterns among different dimensions exactly. The constructed system based on subspace analysis and multidimensional projection visualization can improve the efficiency of analyzing multivariable volume data and guarantee the real-time volume rendering.

Keywords

• Multivariable volume data
• subspace clustering
• RadViz
• volume rendering
• multidimensional projection