Graphical Models for Video Understanding

Having the applications as the ultimate goal, I will demonstrate the algorithmic techniques for speeding up the naive learning in the graphical models by orders of magnitude. In that sense, I will investigate signal processing techniques, approximate methods, and online learning. I will demonstrate how the theory and algorithms usefully apply to the variety of tasks ranging from video clustering and stabilization, to video retrieval and building of the similarity measures between the distributions.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Petrovic, Nemanja D.

Contributors dc:contributor

• Huang, Thomas S.

• Artificial Intelligence

Language dc:language

eng

Identifier

(MiAaPQ)AAI3182351

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/80908