Perception Driven Search Strategies For Effective Multi-Dimensional Visualization

KOCHERLAKOTA, SARAT MOHAN. Perception Driven Search Strategies For Effective Multi-Dimensional Visualization. (Under the direction of Christopher G. Healey) 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 multidimensional 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 an 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. Perception Driven Search Strategies For Effective Multi-Dimensional Visualization by SARAT M. KOCHERLAKOTA A thesis submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the Degree of Master of Science Department of COMPUTER SCIENCE Raleigh, North Carolina 2002 APPROVED BY: Chair of Advisory Committee

[1]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[2]  B. Julesz,et al.  Human factors and behavioral science: Textons, the fundamental elements in preattentive vision and perception of textures , 1983, The Bell System Technical Journal.

[3]  James T. Enns,et al.  Building perceptual textures to visualize multidimensional datasets , 1998, Proceedings Visualization '98 (Cat. No.98CB36276).

[4]  Daryl Pregibon,et al.  Data analysis as search , 1988 .

[5]  Anne Treisman,et al.  Preattentive processing in vision , 1985, Computer Vision Graphics and Image Processing.

[6]  James T. Enns,et al.  Building perceptual textures to visualize multidimensional datasets , 1998 .

[7]  A. Ravishankar Rao,et al.  Identifying High Level Features of Texture Perception , 1993, CVGIP Graph. Model. Image Process..

[8]  Bernice E. Rogowitz,et al.  An architecture for rule-based visualization , 1993, Proceedings Visualization '93.

[9]  Steven K. Feiner,et al.  Introduction to Computer Graphics , 1993 .

[10]  Philip K. Robertson,et al.  A methodology for choosing data representations , 1991, IEEE Computer Graphics and Applications.

[11]  A. Ravishankar Rao,et al.  Towards a texture naming system: Identifying relevant dimensions of texture , 1993, Vision Research.

[12]  Jock D. Mackinlay,et al.  Automating the design of graphical presentations of relational information , 1986, TOGS.

[13]  James T. Enns,et al.  Large Datasets at a Glance: Combining Textures and Colors in Scientific Visualization , 1999, IEEE Trans. Vis. Comput. Graph..

[14]  Christopher G. Healey,et al.  Perceptual Colors and Textures for Scientific Visualization , 1998 .

[15]  Hikmet Senay,et al.  A knowledge-based system for visualization design , 1994, IEEE Computer Graphics and Applications.

[16]  William Knight,et al.  Using visual texture for information display , 1995, TOGS.

[17]  Steven K. Feiner,et al.  AutoVisual: rule-based design of interactive multivariate visualizations , 1993, IEEE Computer Graphics and Applications.

[18]  Christopher G. Healey,et al.  Choosing effective colours for data visualization , 1996, Proceedings of Seventh Annual IEEE Visualization '96.

[19]  Penny Rheingans,et al.  A tool for dynamic explorations of color mappings , 1990, I3D '90.

[20]  Norman Wiseman,et al.  An Introductory Guide to Scientific Visualization , 1992, Springer Berlin Heidelberg.

[21]  W. Cowan,et al.  Visual search for colour targets that are or are not linearly separable from distractors , 1996, Vision Research.

[22]  Clayton Lewis,et al.  A problem-oriented classification of visualization techniques , 1990, Proceedings of the First IEEE Conference on Visualization: Visualization `90.

[23]  Ken Brodlie,et al.  Scientific visualization: techniques and applications , 1992 .

[24]  David Banks,et al.  Image-guided streamline placement , 1996, SIGGRAPH.

[25]  Michael D'Zmura,et al.  Color in visual search , 1991, Vision Research.

[26]  Robert Michael Kirby,et al.  Visualizing multivalued data from 2D incompressible flows using concepts from painting , 1999, VIS '99.

[27]  Neff Walker,et al.  Classifying visual knowledge representations: a foundation for visualization research , 1990, Proceedings of the First IEEE Conference on Visualization: Visualization `90.

[28]  Philip K. Robertson,et al.  A methodology for scientific data visualisation: choosing representations based on a natural scene paradigm , 1990, Proceedings of the First IEEE Conference on Visualization: Visualization `90.