Regularized Adaboost for RGBD video content identification

This thesis presents three contributions. First, we provide an information theoretic analysis to a recently developed learning-based content identification (ID) algorithm, symmetric pairwise boosting (SPB). Second, we propose a regularized Adaboost algorithm, which tackles SPB’s implicit assumption that video segments are statistically independent. Finally, we develop the first hybrid content ID system for synchronized RGB and depth (RGBD) videos. Experimental results show the regularized Adaboost algorithm vastly outperforms SPB for all considered distortions, while the hybrid system further improves the content ID performance of regularized Adaboost relative to RGB-alone or depth-alone content ID systems.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2013

Author dc:creator

• Yu, Honghai

Contributors dc:contributor

• Moulin, Pierre

• Content identification
• fingerprinting
• learning theory
• mutual information
• Kinect camera
• depth video

Statement dc:rights

• Copyright 2012 Honghai Yu

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/42242

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/42242