Mining web images for concept learning

Simple item page
buir.advisorDuygulu, Pınar
dc.contributor.authorGolge, Eren
dc.date.accessioned2015-10-13T08:25:55Z
dc.date.available2015-10-13T08:25:55Z
dc.date.copyright2014-08
dc.date.issued2014-08
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionIncludes bibliographical references (leaves 56-64).en_US
dc.descriptionThesis (M.S.): Bilkent University, The Department of Computer Engineering and the Graduate School of Engineering and Science, 2014.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.description.abstractWe attack the problem of learning concepts automatically from noisy Web image search results. The idea is based on discovering common characteristics shared among category images by posing two novel methods that are able to organise the data while eliminating irrelevant instances. We propose a novel clustering and outlier detection method, namely Concept Map (CMAP). Given an image collection returned for a concept query, CMAP provides clusters pruned from outliers. Each cluster is used to train a model representing a different characteristics of the concept. One another method is Association through Model Evolution (AME). It prunes the data in an iterative manner and it progressively finds better set of images with an evaluational score computed for each iteration. The idea is based on capturing discriminativeness and representativeness of each instance against large number of random images and eliminating the outliers. The final model is used for classification of novel images. These two methods are applied on different benchmark problems and we observed compelling or better results compared to state of art methods.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityby Eren Golge.en_US
dc.format.extentxiii, 64 leaves : illustrations, graphics.en_US
dc.identifier.itemidB148256
dc.identifier.urihttp://hdl.handle.net/11693/14002
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectWeakly-supervised learningen_US
dc.subjectConcept learningen_US
dc.subjectRectifying self-organizing mapen_US
dc.subjectAssociation with model evolutionen_US
dc.subjectClustering and outlier detectionen_US
dc.subjectConceptmapen_US
dc.subjectAttributesen_US
dc.subjectObject recognitionen_US
dc.subjectScene classificationen_US
dc.subjectFace identificationen_US
dc.subjectFeature learningen_US
dc.subject.lccQA76 .G653 2014en_US
dc.subject.lcshComputer science.en_US
dc.subject.lcshData mining.en_US
dc.titleMining web images for concept learningen_US
dc.typeThesisen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0006700-bilkent.pdf
Size:
9.42 MB
Format:
Adobe Portable Document Format
Description:
Full printable version
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Dept. of Computer Engineering - Master's degree