Director of Research (if dissertation) or Advisor (if thesis)
Forsyth, David A.
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(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.
Graduation Semester
2012-05
Permalink
http://hdl.handle.net/2142/32060
Copyright and License Information
Copyright 2012 Mohammad Amin Sadeghi and Ali Farhadi under Creative Commons
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