2016 IEEE Pacific Visualization Symposium (PacificVis) (2016)
April 19, 2016 to April 22, 2016
Richen Liu , Key Laboratory of Machine Perception (Ministry of Education), and School of EECS, Peking University
Hanqi Guo , Mathematics and Computer Science Division, Argonne National Laboratory
Jiang Zhang , Key Laboratory of Machine Perception (Ministry of Education), and School of EECS, Peking University
Xiaoru Yuan , Key Laboratory of Machine Perception (Ministry of Education), and School of EECS, Peking University
We propose a longest common subsequence (LCSS)-based approach to compute the distance among vector field ensembles. By measuring how many common blocks the ensemble pathlines pass through, the LCSS distance defines the similarity among vector field ensembles by counting the number of shared domain data blocks. Compared with traditional methods (e.g., pointwise Euclidean distance or dynamic time warping distance), the proposed approach is robust to outliers, missing data, and the sampling rate of the pathline timesteps. Taking advantage of smaller and reusable intermediate output, visualization based on the proposed LCSS approach reveals temporal trends in the data at low storage cost and avoids tracing pathlines repeatedly. We evaluate our method on both synthetic data and simulation data, demonstrating the robustness of the proposed approach.
Richen Liu, Hanqi Guo, Jiang Zhang, Xiaoru Yuan, "Comparative visualization of vector field ensembles based on longest common subsequence", 2016 IEEE Pacific Visualization Symposium (PacificVis), vol. 00, no. , pp. 96-103, 2016, doi:10.1109/PACIFICVIS.2016.7465256