R2FP: Rich and robust feature pooling for mining visual data

Xiong, W; Du, B; Zhang, L; Hu, R; Bian, W; Shen, J; Tao, D

R2FP: Rich and robust feature pooling for mining visual data

Xiong, W Du, B Zhang, L Hu, R Bian, W Shen, J Tao, D

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Data Mining, ICDM, 2016, 2016-January pp. 469 - 478
Issue Date:: 2016-01-05

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Field	Value	Language
dc.contributor.author	Xiong, W	en_US
dc.contributor.author	Du, B	en_US
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Hu, R	en_US
dc.contributor.author	Bian, W	en_US
dc.contributor.author	Shen, J	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2016-01-05	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Data Mining, ICDM, 2016, 2016-January pp. 469 - 478	en_US
dc.identifier.isbn	9781467395038	en_US
dc.identifier.issn	1550-4786	en_US
dc.identifier.uri	http://hdl.handle.net/10453/92900
dc.description.abstract	© 2015 IEEE. The human visual system proves smart in extracting both global and local features. Can we design a similar way for unsupervised feature learning? In this paper, we propose anovel pooling method within an unsupervised feature learningframework, named Rich and Robust Feature Pooling (R2FP), to better explore rich and robust representation from sparsefeature maps of the input data. Both local and global poolingstrategies are further considered to instantiate such a methodand intensively studied. The former selects the most conductivefeatures in the sub-region and summarizes the joint distributionof the selected features, while the latter is utilized to extractmultiple resolutions of features and fuse the features witha feature balancing kernel for rich representation. Extensiveexperiments on several image recognition tasks demonstratethe superiority of the proposed techniques.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Data Mining, ICDM	en_US
dc.relation.isbasedon	10.1109/ICDM.2015.98	en_US
dc.title	R2FP: Rich and robust feature pooling for mining visual data	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2016-January	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2016-January	en_US

Abstract:

© 2015 IEEE. The human visual system proves smart in extracting both global and local features. Can we design a similar way for unsupervised feature learning? In this paper, we propose anovel pooling method within an unsupervised feature learningframework, named Rich and Robust Feature Pooling (R2FP), to better explore rich and robust representation from sparsefeature maps of the input data. Both local and global poolingstrategies are further considered to instantiate such a methodand intensively studied. The former selects the most conductivefeatures in the sub-region and summarizes the joint distributionof the selected features, while the latter is utilized to extractmultiple resolutions of features and fuse the features witha feature balancing kernel for rich representation. Extensiveexperiments on several image recognition tasks demonstratethe superiority of the proposed techniques.

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http://hdl.handle.net/10453/92900