Towards Automatic Feature-based Visualization

Visualizations are well suited to communicate large amounts of complex data. With increasing resolution in the spatial and temporal domain simple imaging techniques meet their limits, as it is quite difficult to display multiple variables in 3D or analyze long video sequences. Feature-detection techniques reduce the data-set to the essential structures and allow for a highly abstracted representation of the data. However, current feature detection algorithms commonly rely on a detailed description of each individual feature. In this paper, we present a feature-based visualization technique that is solely based on the data. Using concepts from computational mechaniques and information theory, a measure, local statistical complexity, is defined that extracts distinctive structures in the data-set. Local statistical complexity assigns each position in the (multivariate) data-set a scalar value indicating regions with extraordinary behavior. Local structures with high local statistical co...

• Type: Conference
• Year: 2010
• Where: DAGSTUHL
• Authors: Heike Jänicke, Gerik Scheuermann
• Keywords: DAGSTUHL 2010, Local Statistical Complexity, Software Engineering, Statistical Complexity Assigns, Statistical Complexity Form