Mining visual collocation patterns via self-supervised subspace learning
Traditional 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 depend...
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sg-ntu-dr.10356-963252020-03-07T14:02:45Z Mining visual collocation patterns via self-supervised subspace learning Yuan, Junsong Wu, Ying School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Traditional 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. Accepted version 2013-07-15T06:39:31Z 2019-12-06T19:28:59Z 2013-07-15T06:39:31Z 2019-12-06T19:28:59Z 2011 2011 Journal Article Yuan, 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. 1083-4419 https://hdl.handle.net/10356/96325 http://hdl.handle.net/10220/11425 10.1109/TSMCB.2011.2172605 en IEEE transactions on systems, man, and cybernetics, part b (cybernetics) © 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]. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering Yuan, Junsong Wu, Ying Mining visual collocation patterns via self-supervised subspace learning |
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Traditional 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. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Yuan, Junsong Wu, Ying |
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Article |
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Yuan, Junsong Wu, Ying |
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Yuan, Junsong |
title |
Mining visual collocation patterns via self-supervised subspace learning |
title_short |
Mining visual collocation patterns via self-supervised subspace learning |
title_full |
Mining visual collocation patterns via self-supervised subspace learning |
title_fullStr |
Mining visual collocation patterns via self-supervised subspace learning |
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Mining visual collocation patterns via self-supervised subspace learning |
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mining visual collocation patterns via self-supervised subspace learning |
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2013 |
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https://hdl.handle.net/10356/96325 http://hdl.handle.net/10220/11425 |
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