Information theory assisted data visualization and exploration

Official URL: http://192.168.1.20/record=b1416991 (Table of Contents)

This thesis introduces techniques to utilize information theory, particularly entropy for enhancing data visualization and exploration. The ultimate goal with this work is to enable users to perceive as much as information available for recognizing objects, detecting regular or non-regular patterns and reducing user effort while executing the required tasks. We believe that the metrics to be set for enhancing computer generated visualizations should be quantifiable and that quantification should measure the information perception of the user. The proper way to solve this problem is utilizing information theory, particularly entropy. Entropy offers quantification of the information amount in a general communication system. In the communication model, information sender and information receiver are connected with a channel. We are inspired from this model and exploited it in a different way, namely we set the information sender as the data to be visualized, the information receiver as the viewer and the communication channel as the screen where the visualized image is displayed. In this thesis we explore the usage of entropy in three different visualization problems, -Enhancing the visualization of large scale social networks for better perception, -Finding the best representational images of a 3D object to visually inspect with minimal loss of information, -Automatic navigation over a 3D terrain with minimal loss of information. Visualization of large scale social networks is still a major challenge for information visualization researchers. When a thousand nodes are displayed on the screen with the lack of coloring, sizing and filtering mechanisms, the users generally do not perceive much on the first look. They usually use pointing devices or keyboard for zooming and panning to find the information that they are looking for. With this thesis we tried to present a visualization approach that uses coloring, sizing and filtering to help the users recognize the presented information. The second problem that we tried to tackle is finding the best representational images of 3D models. This problem is highly subjective in cognitive manner. The best or good definitions do not depend on any metric or any quantification, furthermore, when the same image is presented to two different users it can be identified differently. However in this thesis we tried to map some metrics to best or good definitions for representational images, such as showing the maximum faces, maximum saliency or combination of both in an image. The third problem that we tried to find a solution is automatic terrain navigation with minimal loss of information. The information to be quantified on this problem is taken as the surface visibility of a terrain. However the visibility problem is changed with the heuristic that users generally focus on city centers, buildings and interesting points during terrain exploration. In order to improve the information amount at the time of navigation, we should focus on those areas. Hence we employed the road network data, and set the heuristic that intersections of road network segments are the residential places. In this problem, region extraction using road network data, viewpoint entropy for camera positions, and automatic camera path generation methods are investigated.