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dc.contributor.advisorMcCane, Brendan
dc.contributor.advisorMills, Steven
dc.contributor.advisorTrotman, Andrew
dc.contributor.authorKhan, Umair Mateen
dc.date.available2017-10-29T20:52:56Z
dc.date.copyright2017
dc.identifier.citationKhan, U. M. (2017). Unsupervised Detection of Emergent Patterns in Large Image Collections (Thesis, Doctor of Philosophy). University of Otago. Retrieved from http://hdl.handle.net/10523/7650en
dc.identifier.urihttp://hdl.handle.net/10523/7650
dc.description.abstractWith the advent of modern image acquisition and sharing technologies, billions of images are added to the Internet every day. This huge repository contains useful information, but it is very hard to analyze. If labeled information is available for this data, then supervised learning techniques can be used to extract useful information. Visual pattern mining approaches provide a way to discover visual structures and patterns in an image collection without the need of any supervision. The Internet contains images of various objects, scenes, patterns, and shapes. The majority of approaches for visual pattern discovery, on the other hand, find patterns that are related to object or scene categories.Emergent pattern mining techniques provide a way to extract generic, complex and hidden structures in images. This thesis describes research, experiments, and analysis conducted to explore various approaches to mine emergent patterns from image collections in an unsupervised way. These approaches are based on itemset mining and graph theoretic strategies. The itemset mining strategy uses frequent itemset mining and rare itemset mining techniques to discover patterns.The mining is performed on a transactional dataset which is obtained from the BoW representation of images. The graph-based approach represents visual word co-occurrences obtained from images in a co-occurrence graph.Emergent patterns form dense clusters in this graph that are extracted using normalized cuts. The patterns that are discovered using itemset mining approaches are:stripes and parallel lines;dots and checks;bright dots;single lines;intersections; and frames. The graph based approach revealed various interesting patterns, including some patterns that are related to object categories.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherUniversity of Otago
dc.rightsAll items in OUR Archive are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectUnsupervised
dc.subjectPatterns
dc.subjectEmergent
dc.subjectImages
dc.titleUnsupervised Detection of Emergent Patterns in Large Image Collections
dc.typeThesis
dc.date.updated2017-10-27T16:23:54Z
dc.language.rfc3066en
thesis.degree.disciplineDepartment of Computer Science
thesis.degree.nameDoctor of Philosophy
thesis.degree.grantorUniversity of Otago
thesis.degree.levelDoctoral
otago.openaccessOpen
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