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Image retrieval using visual attention

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Date Issued:
2008
Summary:
The retrieval of digital images is hindered by the semantic gap. The semantic gap is the disparity between a user's high-level interpretation of an image and the information that can be extracted from an image's physical properties. Content based image retrieval systems are particularly vulnerable to the semantic gap due to their reliance on low-level visual features for describing image content. The semantic gap can be narrowed by including high-level, user-generated information. High-level descriptions of images are more capable of capturing the semantic meaning of image content, but it is not always practical to collect this information. Thus, both content-based and human-generated information is considered in this work. A content-based method of retrieving images using a computational model of visual attention was proposed, implemented, and evaluated. This work is based on a study of contemporary research in the field of vision science, particularly computational models of bottom-up visual attention. The use of computational models of visual attention to detect salient by design regions of interest in images is investigated. The method is then refined to detect objects of interest in broad image databases that are not necessarily salient by design. An interface for image retrieval, organization, and annotation that is compatible with the attention-based retrieval method has also been implemented. It incorporates the ability to simultaneously execute querying by image content, keyword, and collaborative filtering. The user is central to the design and evaluation of the system. A game was developed to evaluate the entire system, which includes the user, the user interface, and retrieval methods.
Title: Image retrieval using visual attention.
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Name(s): Mayron, Liam M.
College of Engineering and Computer Science
Florida Atlantic University
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: multipart monograph
Date Issued: 2008
Publisher: Florida Atlantic University
Physical Form: electronic
Extent: xvii, 217 p. : ill. (some col.).
Language(s): English
Summary: The retrieval of digital images is hindered by the semantic gap. The semantic gap is the disparity between a user's high-level interpretation of an image and the information that can be extracted from an image's physical properties. Content based image retrieval systems are particularly vulnerable to the semantic gap due to their reliance on low-level visual features for describing image content. The semantic gap can be narrowed by including high-level, user-generated information. High-level descriptions of images are more capable of capturing the semantic meaning of image content, but it is not always practical to collect this information. Thus, both content-based and human-generated information is considered in this work. A content-based method of retrieving images using a computational model of visual attention was proposed, implemented, and evaluated. This work is based on a study of contemporary research in the field of vision science, particularly computational models of bottom-up visual attention. The use of computational models of visual attention to detect salient by design regions of interest in images is investigated. The method is then refined to detect objects of interest in broad image databases that are not necessarily salient by design. An interface for image retrieval, organization, and annotation that is compatible with the attention-based retrieval method has also been implemented. It incorporates the ability to simultaneously execute querying by image content, keyword, and collaborative filtering. The user is central to the design and evaluation of the system. A game was developed to evaluate the entire system, which includes the user, the user interface, and retrieval methods.
Identifier: 231745692 (oclc), 58006 (digitool), FADT58006 (IID), fau:4292 (fedora)
Note(s): by Liam M. Mayron.
Thesis (Ph.D.)--Florida Atlantic University, 2008.
Includes bibliography.
Electronic reproduction. Boca Raton, FL : 2008 Mode of access: World Wide Web.
Subject(s): Image processing -- Digital techniques
Database systems
Cluster analysis
Multimedia systems
Persistent Link to This Record: http://purl.flvc.org/fcla/flaent/EN00154040/68_1/98p0137i.pdf
Persistent Link to This Record: http://purl.flvc.org/FAU/58006
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU