RUcore: Rutgers University Community Repository
Towards automated classification of fine-art painting style
Descriptive
TitleInfo
Title
Towards automated classification of fine-art painting style
SubTitle
a comparative study
Name
(type = personal)
NamePart
(type = family)
Arora
NamePart
(type = given)
Ravneet Singh
NamePart
(type = date)
1985-
DisplayForm
Ravneet Arora
Role
RoleTerm
(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
Elgammal
NamePart
(type = given)
Ahmed
DisplayForm
Ahmed Elgammal
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
chair
Name
(type = personal)
NamePart
(type = family)
Pavlovic
NamePart
(type = given)
Vladimir
DisplayForm
Vladimir Pavlovic
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
co-chair
Name
(type = personal)
NamePart
(type = family)
Kulikowski
NamePart
(type = given)
Casimir
DisplayForm
Casimir Kulikowski
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
co-chair
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 thesis presents a comparative study of different classification methodologies for the task of fine-art genre classification. The problem of painting classification involves classifying new unknown paintings among different art genres. Two-level comparative study is performed for this classification problem. The first level reviews the performance of discriminative vs. generative models while the second level touches the features aspect of the paintings and compares Semantic-level features vs low-level and intermediate-level features present in the painting. Three models are studied and compared, namely - 1) A Discriminative model using a Bag-of-Words (BoW) approach; 2) A Generative model using BoW; 3) Discriminative model using Semantic-level features. Various experiments and techniques like Bag of Words model, Topic models and Classeme features are employed to get insights into potential of these automatic classification techniques for painting styles.
Subject
(authority = RUETD)
Topic
Computer Science
RelatedItem
(type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
Identifier
ETD_4243
PhysicalDescription
Form
(authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
vii, 45 p. : ill.
Note
(type = degree)
M.S.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = statement of responsibility)
by Ravneet Singh Arora
Subject
(authority = ETD-LCSH)
Topic
Painting--Classification
Subject
(authority = ETD-LCSH)
Topic
Computer vision
Subject
(authority = ETD-LCSH)
Topic
Pattern recognition systems
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066603
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/T3XP73QP
Genre
(authority = ExL-Esploro)
ETD graduate
Back to the top
Rights
RightsDeclaration
(ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder
(type = personal)
Name
FamilyName
Arora
GivenName
Ravneet
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
(encoding = w3cdtf);
(qualifier = exact);
(point = start)
2012-09-16 15:51:26
AssociatedEntity
Name
Ravneet Arora
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
Back to the top
Technical
FileSize
(UNIT = bytes)
997888
OperatingSystem
(VERSION = 5.1)
windows xp
ContentModel
ETD
MimeType
(TYPE = file)
application/pdf
MimeType
(TYPE = container)
application/x-tar
FileSize
(UNIT = bytes)
1003520
Checksum
(METHOD = SHA1)
4bbebd8d0f327040c0c2cee6d74770d2033a2d0d
Back to the top
Statistical Profile