Perception Driven Search Strategies For Effective Multi-Dimensional Visualization

Abstract

Tracking and analysing large amounts of information in many different application areas is a critical problem. One approach to address this problem, is the use of multi-dimensional visualizations to represent large datasets. Visualizations can be constructed effectively by the use of visual features and properties like color and texture. Our objective is to construct multi-dimensional visualizations using perceptually salient visual features which support rapid visual analysis and exploration of large datasets. We use a visualization system called ViA use to construct effective visualizations.

We present a search technique incorporated in ViA, that finds effective attribute-feature mappings to represent multi-dimensional datasets in a perceptually salient fashion. ViA evaluates the salience of attribute-feature mappings using evaluation engines. These evaluation engines also suggest hints that recommend how the mapping can be improved perceptually. The search technique we developed, uses dataset properties, and the hints generated by the evaluation engines to quickly and efficiently produce perceptually salient mappings.

Perceptual guidlines were established from studies and experiments on human perception. ViA works as a semi-automated visualization system that uses effective search technique to find salient mappings. Applying ViA to practical datasets indeed proves the effectiveness of ViA. We think ViA can also produce salient visualizations in a variety domain areas since the guidelines for generation of effective visualizations are based on human perception.