INFORMATION THEORY ASSISTED DATA VISUALIZATION AND EXPLORATION
EECS, Ph.D. Dissertation, 2012
Asst. Prof. Dr.Selim Balcısoy(Thesis Supervisor), Assoc.Prof.Dr.Berrin Yanıkoğlu,
Prof.Dr.Mustafa Ünel, Prof.Dr.Tanju Erdem,
Date &Time: January 18th, 2012 - 10:30
Place: FENS 2019
Keywords: social network, viewpoint entropy, mesh saliency entropy, automatic navigation, evolutionary programming
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. Because the entropy offers quantification of the information amount in a general communication system. In this 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; 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 explored 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, and Automatic navigation over a 3D terrain with minimal loss of information.