Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/24273
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dc.coverage.spatialInformation and Communication Engineeringen_US
dc.date.accessioned2014-09-01T06:48:06Z-
dc.date.available2014-09-01T06:48:06Z-
dc.date.issued2014-09-01-
dc.identifier.urihttp://hdl.handle.net/10603/24273-
dc.description.abstractNowadays finding specific digital images from large resources has become an active research area An image retrieval system is a computer based system for browsing searching and retrieving images from a large database of digital images Among image retrieval approaches text based retrieval is widely used commercially for retrieving But it is not effective as it newlineinvolves time consuming text annotation process Also different users tend to use different keywords to describe a same image characteristic Content Based Image Retrieval is another method of retrieving images from large image resources which has been found to be very effective The term content based means that the search will analyze the actual contents of the newlineimage CBIR system uses low level image features like color texture shape spatial location etc to represent images in terms of their features To improve existing CBIR performance it is very important to find effective and efficient feature extraction mechanisms This research aims to improve the performance of CBIR using region and statistical features newline newlineen_US
dc.format.extentxxi, 128p.en_US
dc.languageEnglishen_US
dc.relation-en_US
dc.rightsuniversityen_US
dc.titleContent based image retrieval system with region and statistical features using evolutionary algorithmsen_US
dc.title.alternative-en_US
dc.creator.researcherSankar Ganesh, Sen_US
dc.subject.keywordContent Based Image Retrievalen_US
dc.subject.keywordDigital imagesen_US
dc.subject.keywordEvolutionary algorithmsen_US
dc.subject.keywordGenetic Algorithmen_US
dc.subject.keywordImage retrieval systemen_US
dc.subject.keywordInformation and communication engineeringen_US
dc.subject.keywordParticle Swarm Optimizationen_US
dc.description.noteReferences p.119-125en_US
dc.contributor.guideRamar, Ken_US
dc.publisher.placeChennaien_US
dc.publisher.universityAnna Universityen_US
dc.publisher.institutionFaculty of Information and Communication Engineeringen_US
dc.date.registeredn.d.en_US
dc.date.completed01/05/2012en_US
dc.date.awarded30/05/2012en_US
dc.format.dimensions23cm.en_US
dc.format.accompanyingmaterialNoneen_US
dc.source.universityUniversityen_US
dc.type.degreePh.D.en_US
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificate.pdf481.98 kBAdobe PDFView/Open
03_abstract.pdf23.7 kBAdobe PDFView/Open
04_acknowledgement.pdf22.81 kBAdobe PDFView/Open
05_contents.pdf53.54 kBAdobe PDFView/Open
06_chapter1.pdf534.7 kBAdobe PDFView/Open
07_chapter2.pdf24.02 kBAdobe PDFView/Open
08_chapter3.pdf949.42 kBAdobe PDFView/Open
09_chapter4.pdf76.99 kBAdobe PDFView/Open
10_chapter5.pdf84.94 kBAdobe PDFView/Open
11_chapter6.pdf2.12 MBAdobe PDFView/Open
12_chapter7.pdf24.17 kBAdobe PDFView/Open
13_references.pdf42.85 kBAdobe PDFView/Open
14_publications.pdf23.81 kBAdobe PDFView/Open
15_vitae.pdf20.68 kBAdobe PDFView/Open


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