Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96325
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dc.contributor.authorYuan, Junsongen
dc.contributor.authorWu, Yingen
dc.date.accessioned2013-07-15T06:39:31Zen
dc.date.accessioned2019-12-06T19:28:59Z-
dc.date.available2013-07-15T06:39:31Zen
dc.date.available2019-12-06T19:28:59Z-
dc.date.copyright2011en
dc.date.issued2011en
dc.identifier.citationYuan, J., & Wu, Y. (2012). Mining Visual Collocation Patterns via Self-Supervised Subspace Learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2), 334-346.en
dc.identifier.issn1083-4419en
dc.identifier.urihttps://hdl.handle.net/10356/96325-
dc.description.abstractTraditional text data mining techniques are not directly applicable to image data which contain spatial information and are characterized by high-dimensional visual features. It is not a trivial task to discover meaningful visual patterns from images because the content variations and spatial dependence in visual data greatly challenge most existing data mining methods. This paper presents a novel approach to coping with these difficulties for mining visual collocation patterns. Specifically, the novelty of this work lies in the following new contributions: 1) a principled solution to the discovery of visual collocation patterns based on frequent itemset mining and 2) a self-supervised subspace learning method to refine the visual codebook by feeding back discovered patterns via subspace learning. The experimental results show that our method can discover semantically meaningful patterns efficiently and effectively.en
dc.language.isoenen
dc.relation.ispartofseriesIEEE transactions on systems, man, and cybernetics, part b (cybernetics)en
dc.rights© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TSMCB.2011.2172605].en
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleMining visual collocation patterns via self-supervised subspace learningen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.identifier.doi10.1109/TSMCB.2011.2172605en
dc.description.versionAccepted versionen
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