FlowString: Partial streamline matching using shape invariant similarity measure for exploratory flow visualization
Department of Computer Science
Measuring the similarity of integral curves is fundamental to many important flow data analysis and visualization tasks such as feature detection, pattern querying, streamline clustering and hierarchical exploration. In this paper, we introduce FlowString, a novel approach that extracts shape invariant features from streamlines and utilizes a string-based method for exploratory streamline analysis and visualization. Our solution first resamples streamlines by considering their local feature scales. We then classify resampled points along streamlines based on the shape similarity around their local neighborhoods. We encode each streamline into a string of well-selected shape characters, from which we construct meaningful words for querying and retrieval. A unique feature of our approach is that it captures intrinsic streamline similarity that is invariant under translation, rotation and scaling. Leveraging the suffix tree, we enable efficient search of streamline patterns with arbitrary lengths with the complexity linear to the size of the respective pattern. We design an intuitive interface and user interactions to support flexible querying, allowing exact and approximate searches for robust partial streamline similarity matching. Users can perform queries at either the character level or the word level, and define their own characters or words conveniently for customized search. We demonstrate the effectiveness of FlowString with several flow field data sets of different sizes and characteristics.
2014 IEEE Pacific Visualization Symposium
FlowString: Partial streamline matching using shape invariant similarity measure for exploratory flow visualization.
2014 IEEE Pacific Visualization Symposium.
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/899