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Title:Recognition using visual phrases
Author(s):Sadeghi, Mohammad Amin
Advisor(s):Forsyth, David A.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Visual Phrase
Phrasal Recognition
Visual Composites
Object Recognition
Abstract:In this thesis I introduce visual phrases, complex visual composites like ``a person riding a horse''. Visual phrases often display significantly reduced visual complexity compared to their component objects, because the appearance of those objects can change profoundly when they participate in relations. I introduce a dataset suitable for phrasal recognition that uses familiar PASCAL object categories, and demonstrate significant experimental gains resulting from exploiting visual phrases. I show that a visual phrase detector significantly outperforms a baseline which detects component objects and reasons about relations, even though visual phrase training sets tend to be smaller than those for objects. I argue that any multi-class detection system must decode detector outputs to produce final results; this is usually done with non-maximum suppression. I describe a novel decoding procedure that can account accurately for local context without solving difficult inference problems. I show this decoding procedure outperforms the state of the art. Finally, I show that decoding a combination of phrasal and object detectors produces real improvements in detector results.
Issue Date:2012-06-27
Rights Information:Copyright 2012 Mohammad Amin Sadeghi and Ali Farhadi under Creative Commons
Date Available in IDEALS:2014-06-28
Date Deposited:2012-05

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