Date of Award

January 2016

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical and Computer Engineering

First Advisor

Jan P Allebach

Committee Member 1

Mireille Boutin

Committee Member 2

Mary L Comer

Committee Member 3

Edward J Delp

Committee Member 4

Qian Lin

Abstract

Understanding the visual content of images is one of the most important topics in computer vision. Many researchers have tried to teach the machine to see and perceive like human. In this dissertation, we develop several new approaches for image understanding with applications to affective computing, and person detection and recognition. Our proposed method applied to fashion photo analysis can understand the aesthetic quality of photos. Further, a bilinear model that takes into account the relative confidence of region proposals and the mutual relationship between multiple labels is developed to boost multi-label classification. It is evaluated both on object recognition and aesthetic attributes learning. We also develop a person detection and recognition system in natural settings that can robustly handle various pose, viewpoints, and lighting conditions. The system is then put into several real scenarios that has different amount of labelled data. Our algorithm that utilizes unlabelled data reduces the effort needed for data annotation while achieving similar results as with labelled data.

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