Abstract

Introduction to Information Theory for Vision

Ying Nian Wu
University of California, Los Angeles (UCLA)
Statistics

This lecture reviews key concepts in information theory, such as entropy and Kullback-Leibler divergence, as well as the basic ideas of information geometry. The lecture will then describe the applications of information theory in vision, such as maximum entropy models and performance bounds.