Bag of Features using sparse coding for gender classification

Peng, Y; Luo, S; Xu, M; Ni, Z; Jin, JS; Wang, J; Zhao, G

Bag of Features using sparse coding for gender classification

Peng, Y Luo, S Xu, M

Ni, Z Jin, JS Wang, J Zhao, G

Permalink

Publication Type:: Conference Proceeding
Citation:: ACM International Conference Proceeding Series, 2012, pp. 80 - 83
Issue Date:: 2012-11-19

Closed Access

	Filename	Description	Size
	2012004229OK.pdf		860.67 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Peng, Y	en_US
dc.contributor.author	Luo, S	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.contributor.author	Ni, Z	en_US
dc.contributor.author	Jin, JS	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Zhao, G	en_US
dc.date.issued	2012-11-19	en_US
dc.identifier.citation	ACM International Conference Proceeding Series, 2012, pp. 80 - 83	en_US
dc.identifier.isbn	9781450316002	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22944
dc.description.abstract	Gender classification is challenging. Methods for gender classification need to discriminate subtle differences between male and female. Bag-of-Features (BoF) method with sparse coding has been proven very powerful in image classification. In this paper, we apply BoF method for gender classification. We use two sets of images: training images and testing images. All images are represented by a set of Scale-Invariant Feature Transform (SIFT) descriptors. In training stage, using sparse coding, Visual Words Dictionary (VWD) is constructed from SIFT descriptors extracted from training images. In testing, SIFT descriptors of testing images are approximated by visual words in VWD. The choices of approximating visual words determine the classification decision. We apply our method and another two popular methods on public dataset for gender classification. We achieved promising results. Copyright © 2012 ACM.	en_US
dc.relation.ispartof	ACM International Conference Proceeding Series	en_US
dc.relation.isbasedon	10.1145/2382336.2382359	en_US
dc.title	Bag of Features using sparse coding for gender classification	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Wuhan China	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Gender classification is challenging. Methods for gender classification need to discriminate subtle differences between male and female. Bag-of-Features (BoF) method with sparse coding has been proven very powerful in image classification. In this paper, we apply BoF method for gender classification. We use two sets of images: training images and testing images. All images are represented by a set of Scale-Invariant Feature Transform (SIFT) descriptors. In training stage, using sparse coding, Visual Words Dictionary (VWD) is constructed from SIFT descriptors extracted from training images. In testing, SIFT descriptors of testing images are approximated by visual words in VWD. The choices of approximating visual words determine the classification decision. We apply our method and another two popular methods on public dataset for gender classification. We achieved promising results. Copyright © 2012 ACM.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/22944