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dc.contributor.advisorPotetz, Brian
dc.contributor.authorKroge, Jason
dc.date.accessioned2010-06-09T03:52:24Z
dc.date.available2010-06-09T03:52:24Z
dc.date.issued2010-04-28
dc.date.submitted2010
dc.identifier.otherhttp://dissertations.umi.com/ku:10898
dc.identifier.urihttp://hdl.handle.net/1808/6294
dc.description.abstractWith the shear amount and variety of digital images available in the world today, people need an effective method to search for any particular image. The commonly used strategy of searching by keyword has several problems, especially when searching for aspects that are difficult to describe with words. In this paper, I will discuss an image retrieval system that can be used to search for visually-similar images based on image content rather than associated keywords. I will discuss the major components of this system including a pre-processing step using Haar wavelets and the steps for training a deep belief network to recognize higher-order features that may have a semantic or category specific meaning. The paper concludes with a comparison of performance between the newly proposed system and other published results.
dc.format.extent26 pages
dc.language.isoEN
dc.publisherUniversity of Kansas
dc.rightsThis item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
dc.subjectComputer science
dc.titleContent-Based Image Retrieval Using Deep Belief Networks
dc.typeThesis
dc.contributor.cmtememberChen, Xue-wen
dc.contributor.cmtememberLuo, Bo
dc.thesis.degreeDisciplineElectrical Engineering & Computer Science
dc.thesis.degreeLevelM.S.
kusw.oastatusna
kusw.oapolicyThis item does not meet KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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