Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96325
Title: Mining visual collocation patterns via self-supervised subspace learning
Authors: Yuan, Junsong
Wu, Ying
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2011
Source: 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.
Series/Report no.: IEEE transactions on systems, man, and cybernetics, part b (cybernetics)
Abstract: 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.
URI: https://hdl.handle.net/10356/96325
http://hdl.handle.net/10220/11425
ISSN: 1083-4419
DOI: http://dx.doi.org/10.1109/TSMCB.2011.2172605
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].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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