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dc.contributor.advisorDeng, Jeremiah D.
dc.contributor.advisorPurvis, Martin K.
dc.contributor.authorGu, Xianbin
dc.date.available2017-06-29T02:15:49Z
dc.date.copyright2017
dc.identifier.citationGu, X. (2017). Image segmentation using superpixel ensembles (Thesis, Doctor of Philosophy). University of Otago. Retrieved from http://hdl.handle.net/10523/7403en
dc.identifier.urihttp://hdl.handle.net/10523/7403
dc.description.abstractRecently there has been an increasing interest in image segmentation due to the needs of locating objects with high segmentation accuracy as required by many computer vision and image processing tasks. While image segmentation remains a research challenge, 'superpixel' as the perceptual meaningful grouping of pixels has become a popular concept and a number of superpixel-based image segmentation algorithms have been proposed. The goal of this thesis is to examine the state-of-the-art superpixel algorithms and introduce new methods for achieving better image segmentation outcome. To improve the accuracy of superpixel-based segmentation, we propose a colour covariance matrix-based segmentation algorithm (CCM). This algorithm employs a novel colour covariance descriptor and a corresponding similarity measure method. Moreover, based on the CCM algorithm, we propose a multi-layer bipartite graph model (MBG-CCM) and a low-rank representation technique based algorithm (LRR-CCM). In MBG-CCM, different superpixel descriptors are fused by a multi-layer bipartite graph, and in LRR-CCM, the similarities of the covariance descriptors of the superpixel are measured by the subspace structure. Besides, we develop a new over-segmentation, called superpixel association, and propose a novel segmentation algorithm (SHST) which is able to generate hierarchical segmentation from superpixel associations. In addition to those unsupervised segmentation algorithms, we also explore the algorithms for supervised segmentation. We propose a model for semantic segmentation, named 'generalized puzzle game', by which the segmentation information contained in the superpixels can be integrated into the supervised segmentation.
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.subjectimage segmentation
dc.subjectsuperpixel
dc.subjectfeature fusion
dc.subjectensemble clustering
dc.titleImage segmentation using superpixel ensembles
dc.typeThesis
dc.date.updated2017-06-29T01:08:14Z
dc.language.rfc3066en
thesis.degree.disciplineInformation Science
thesis.degree.nameDoctor of Philosophy
thesis.degree.grantorUniversity of Otago
thesis.degree.levelDoctoral
otago.openaccessOpen
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