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Towards a local-global visual feature-based framework for recognition

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Text
TitleInfo (ID = T-1)
Title
Towards a local-global visual feature-based framework for recognition
SubTitle
PartName
PartNumber
NonSort
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ETD_2075
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051935
Language (objectPart = )
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eng
Genre (authority = marcgt)
theses
Subject (ID = SBJ-1); (authority = RUETD)
Topic
Computer Science
Subject (ID = SBJ-2); (authority = ETD-LCSH)
Topic
Computer vision
Abstract
General object and activity recognition is a fundamental problem in computer vision that has been the subject of much research. Traditional approaches include model based and appearance template based methods. Recently, inspired by methods from the text retrieval literature, local visual feature-based models have shown a lot of success for recognition of objects or activities with large within-class geometric variability.
There are several challenges in this approach, namely feature selection and target modeling using these features. This thesis proposes a local-global visual feature-based framework for general object and activity recognition with novel methods for these problems:
1) Combinatorial and statistical methods for selecting informative parts to build statistical models for part-based object recognition. First a combinatorial optimization formulation is used for clustering on a weighted multipartite graph. Second, a statistical method for selecting discriminative parts from positive images is used to localize objects.
2) An entropy based vocabulary selection method for “bag-of-words” model for activity recognition.
3) Integrating both spatial and temporal information with appearance feature for human activity recognition. This method models the human motions with the distribution of local motion features and their spatial-temporal arrangements.
The effectiveness of the proposed methods is demonstrated by several object recognition and activity recognition data sets, which include human facial expressions and hand gestures, etc.
This thesis also covers an interesting project regarding a framework of applying Discrete Fourier Transform to detect salient regions in images and video sequences. This framework generalizes the previous saliency detection methods and can be applied for saliency detection in the video sequences.
PhysicalDescription
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electronic resource
Extent
xiii, 105 p. : ill.
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Ph.D.
Note (type = bibliography)
Includes bibliographical references (p. 99-104)
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by Zhipeng Zhao
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Zhao
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Zhipeng
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1973-
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Zhipeng Zhao
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Ahmed
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chair
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Ahmed Elgammal
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Kulikowski
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Casimir
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internal member
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Advisory Committee
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Casimir Kulikowski
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Pavlovic
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Vladimir
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internal member
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Vladimir Pavlovic
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Shet
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Vinay
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outside member
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Advisory Committee
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Vinay D Shet
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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
OriginInfo
DateCreated (point = ); (qualifier = exact)
2009
DateOther (qualifier = exact); (type = degree)
2009-10
Place
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xx
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3BK1CHQ
Genre (authority = ExL-Esploro)
ETD doctoral
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The author owns the copyright to this work
Copyright
Status
Copyright protected
Notice
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Status
Open
Reason
Permission or license
Note
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Zhao
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Zhipeng
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Zhipeng Zhao
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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