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Selection-based dictionary learning for sparse representation in visual tracking

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TitleInfo
Title
Selection-based dictionary learning for sparse representation in
visual tracking
Name (type = personal)
NamePart (type = family)
Liu
NamePart (type = given)
Baiyang
NamePart (type = date)
1983-
DisplayForm
Baiyang Liu
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Kulikowski
NamePart (type = given)
Casimir A
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Casimir A Kulikowski
Affiliation
Advisory Committee
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chair
Name (type = personal)
NamePart (type = family)
Elgammal
NamePart (type = given)
Ahmed
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Ahmed Elgammal
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Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Pavlovic
NamePart (type = given)
Vladimir
DisplayForm
Vladimir Pavlovic
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Comaniciu
NamePart (type = given)
Dorin
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Dorin Comaniciu
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2012
DateOther (qualifier = exact); (type = degree)
2012-10
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
This dissertation describes a novel selection-based dictionary learning method with a sparse representation to tackle the object tracking problem in computer vision. The sparse representa- tion has been widely used in many applications including visual tracking, compressive sensing, image de-noising and image classification, and learning a good dictionary for the sparse rep- resentation is critical for obtaining high performance. The most popular existing dictionary learning algorithms are generalized from K-means, which compute the dictionary columns to minimize the overall target reconstruction error iteratively. For better discriminative capability to differentiate target-object (positive) from background (negative) data, a class of dictionary algorithms has been developed to learn the dictionary from both the positive and the negative data. However, these methods do not work well for visual tracking in a dynamic environment in which the background can change considerably between frames in a non-linear way. The background cannot be modeled statically with the usual linear models. In this tdissertation, I report on the development of a selection-based dictionary learning algorithm (K-Selection) that constructs the dictionary by choosing its columns from the training data. Each column is the most representative basis for the whole dataset, which also has a clear physical meaning. With locality-constraints, the subspace represented by the learned dictionary is not restricted to the training data alone, and is also less sensitive to outliers. The sparse representation based on this dictionary learning method supports a more robust tracker trained on the target-object data alone. This is because the learned dictionary has more discriminative power and can better distinguish the object from the background clutter. By extending the dictionary with encoded spatial information, I present a new tracking algorithm which is robust to dynamic appearance changes and occlusions. The performance of the proposed algorithms have been validated for several challenging visual tracking applications through a series of comparative experiments.
Subject (authority = RUETD)
Topic
Computer Science
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_4314
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
xi, 79 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Baiyang Liu
Subject (authority = ETD-LCSH)
Topic
Computer vision
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066893
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T3BZ64S3
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Liu
GivenName
Baiyang
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2012-10-03 09:09:23
AssociatedEntity
Name
Baiyang Liu
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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