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Hypergraph based visual categorization and segmentation
Descriptive
TypeOfResource
Text
TitleInfo
(ID = T-1)
Title
Hypergraph based visual categorization and segmentation
Identifier
ETD_2782
Identifier
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http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056373
Language
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eng
Genre
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theses
Subject
(ID = SBJ-1);
(authority = RUETD)
Topic
Computer Science
Subject
(ID = SBJ-2);
(authority = ETD-LCSH)
Topic
Hypergraphs
Subject
(ID = SBJ-3);
(authority = ETD-LCSH)
Topic
Computer vision--Mathematical models
Abstract
(type = abstract)
This dissertation explores original techniques for the construction of hypergraph models for computer vision applications. A hypergraph is a generalization of a pairwise simple graph, where an edge can connect any number of vertices. The expressive power of the hypergraph models places a special emphasis on the relationship among three or more objects, which has
made hypergraphs better models of choice in a lot of problems. This is in sharp contrast with the more conventional graph representation of visual patterns where only pairwise connectivity between objects is described. The contribution of this thesis is fourfold: (i) For the first time the advantage of the hypergraph neighborhood structure is analyzed. We argue that the summarized local grouping information contained in hypergraphs causes an ‘averaging’ effect which is beneficial to the clustering problems, just as local image smoothing may be beneficial to the image segmentation task. (ii) We discuss how to build hypergraph incidence structures and how to solve the related unsupervised and semi-supervised problems for three different computer vision scenarios:
video object segmentation, unsupervised image categorization and image retrieval. We compare
our algorithms with state-of-the-art methods and the effectiveness of the proposed methods is demonstrated by extensive experimentation on various datasets. (iii) For the application of image retrieval, we propose a novel hypergraph model — probabilistic
hypergraph to exploit the structure of the data manifold by considering not only the local grouping information, but also the similarities between vertices in hyperedges. (iv) In all three applications mentioned above, we conduct an in depth comparison between
simple graph and hypergraph based algorithms, which is also beneficial to other computer vision applications.
made hypergraphs better models of choice in a lot of problems. This is in sharp contrast with the more conventional graph representation of visual patterns where only pairwise connectivity between objects is described. The contribution of this thesis is fourfold: (i) For the first time the advantage of the hypergraph neighborhood structure is analyzed. We argue that the summarized local grouping information contained in hypergraphs causes an ‘averaging’ effect which is beneficial to the clustering problems, just as local image smoothing may be beneficial to the image segmentation task. (ii) We discuss how to build hypergraph incidence structures and how to solve the related unsupervised and semi-supervised problems for three different computer vision scenarios:
video object segmentation, unsupervised image categorization and image retrieval. We compare
our algorithms with state-of-the-art methods and the effectiveness of the proposed methods is demonstrated by extensive experimentation on various datasets. (iii) For the application of image retrieval, we propose a novel hypergraph model — probabilistic
hypergraph to exploit the structure of the data manifold by considering not only the local grouping information, but also the similarities between vertices in hyperedges. (iv) In all three applications mentioned above, we conduct an in depth comparison between
simple graph and hypergraph based algorithms, which is also beneficial to other computer vision applications.
PhysicalDescription
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electronic resource
Extent
xiv, 99 p. : ill.
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text/xml
Note
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Ph.D.
Note
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Includes bibliographical references
Note
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Includes vita
Note
(type = statement of responsibility)
by Yuchi Huang
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Huang
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Yuchi
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1979-
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author
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Yuchi Huang
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Metaxas
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Dimitris N.
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chair
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Dimitris N. Metaxas
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Ahmed
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Advisory Committee
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Ahmed Elgammal
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Pavlovic
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Vladimir
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Advisory Committee
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Vladimir Pavlovic
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Kambhamettu
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Chandra
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Advisory Committee
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Chandra Kambhamettu
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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school
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2010
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2010
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xx
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Rutgers University Electronic Theses and Dissertations
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ETD
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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NjNbRU
Identifier
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doi:10.7282/T3TT4QQF
Genre
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ETD doctoral
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Rights
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The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
RightsHolder
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(type = personal)
Name
FamilyName
Huang
GivenName
Yuchi
Role
Copyright Holder
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Permission or license
DateTime
2010-07-18 23:40:40
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Name
Yuchi Huang
Affiliation
Rutgers University. Graduate School - New Brunswick
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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.
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