Skip to Main Content
Frequently Asked Questions
Submit an ETD
Global Search Box
Need Help?
Keyword Search
Participating Institutions
Advanced Search
School Logo
Files
File List
osu1285011830.pdf (2.62 MB)
ETD Abstract Container
Abstract Header
Viewpoint Independent Image Classification and Retrieval
Author Info
Ozendi, Mustafa
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=osu1285011830
Abstract Details
Year and Degree
2010, Master of Science, Ohio State University, Geodetic Science and Surveying.
Abstract
Image retrieval has applications in different disciplines. For example, there are applications in digital painting catalogues and in security related applications Researchers from both computer vision and photogrammetry fields are developing robust image retrieval methods that can be used for achieving, browsing and searching. Various approaches have been developed by researchers to solve the retrieval problem using different image features, including color, texture and shape of objects in the image. Our method is motivated from a geometric invariance framework, which is based on invariance of conic sections under the projective image transformation. First, conic sections, which are fitted to object boundaries, are generated. The invariance property of these conic sections is used to represent the shape of the object boundaries. This representation provides an invariant signature of that image. Once an invariant signature is obtained for each image, certain classification methods are used to test whether these signatures present unique characteristics for each image group. Additionally, a retrieval mechanism is built that uses invariant signatures of each image to build a relationship with other images and to retrieve the most related ones. A measure of the relationship between images is obtained by using two common metrics histogram intersection and minimum pair distance assignment. It is hypothesized in this research that generated invariant signatures present unique characteristics for each image group and these signatures can be used for classification and retrieval of images in a database. This hypothesis is satisfied in terms of classification, but it is not satisfied for retrieval problems because of degenerate conics.
Committee
Alper Yilmaz (Advisor)
Carolyn Merrry (Committee Member)
Pages
127 p.
Subject Headings
Computer Science
Keywords
image retrieval
;
image classification
;
projective invariant
;
viewpoint independent retrieval
;
viewpoint independent image classification
Recommended Citations
Refworks
EndNote
RIS
Mendeley
Citations
Ozendi, M. (2010).
Viewpoint Independent Image Classification and Retrieval
[Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1285011830
APA Style (7th edition)
Ozendi, Mustafa.
Viewpoint Independent Image Classification and Retrieval.
2010. Ohio State University, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=osu1285011830.
MLA Style (8th edition)
Ozendi, Mustafa. "Viewpoint Independent Image Classification and Retrieval." Master's thesis, Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1285011830
Chicago Manual of Style (17th edition)
Abstract Footer
Document number:
osu1285011830
Download Count:
790
Copyright Info
© 2010, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.