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Title:Regularized Adaboost for RGBD video content identification
Author(s):Yu, Honghai
Advisor(s):Moulin, Pierre
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Content identification
learning theory
mutual information
Kinect camera
depth video
Abstract: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.
Issue Date:2013-02-03
Rights Information:Copyright 2012 Honghai Yu
Date Available in IDEALS:2013-02-03
Date Deposited:2012-12

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