More on weak feature: Self-correlate histogram distances

Wang, S; Wu, Q; He, X; Jia, W

More on weak feature: Self-correlate histogram distances

Wang, S Wu, Q

He, X

Jia, W

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 7087 LNCS (PART1), pp. 214 - 223
Issue Date:: 2011-11-30

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Field	Value	Language
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.date.issued	2011-11-30	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 7087 LNCS (PART1), pp. 214 - 223	en_US
dc.identifier.isbn	9783642253669	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31267
dc.description.abstract	In object detection research, there is a discussion on weak feature and strong feature, feature descriptors, regardless of being considered as 'weak feature descriptors' or 'strong feature descriptors' does not necessarily imply detector performance unless combined with relevant classification algorithms. Since 2001, main stream object detection research projects have been following the Viola Jone's weak feature (Haar-like feature) and AdaBoost classifier approach. Until 2005, when Dalal and Triggs have created the approach of a strong feature (Histogram of Oriented Gradient) and Support Vector Machine (SVM) framework for human detection. This paper proposes an approach to improve the salience of a weak feature descriptor by using intra-feature correlation. Although the intensity histogram distance feature known as Histogram Distance of Haar Regions (HDHR) itself is considered as a weak feature and can only be used to construct a weak learner to learn an AdaBoost classifier. In our paper, we explore the pairwise correlations between each and every histograms constructed and a strong feature can then be formulated. With the newly constructed strong feature based on histogram distances, a SVM classifier can be trained and later used for classification tasks. Promising experimental results have been obtained. © 2011 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-25367-6_19	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	More on weak feature: Self-correlate histogram distances	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART1	en_US
utslib.citation.volume	7087 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Gwangju, South Korea
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/Strength - CRIN - Realtime Information Networks
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.issue	PART1	en_US
pubs.publication-status	Published	en_US
pubs.volume	7087 LNCS	en_US

Abstract:

In object detection research, there is a discussion on weak feature and strong feature, feature descriptors, regardless of being considered as 'weak feature descriptors' or 'strong feature descriptors' does not necessarily imply detector performance unless combined with relevant classification algorithms. Since 2001, main stream object detection research projects have been following the Viola Jone's weak feature (Haar-like feature) and AdaBoost classifier approach. Until 2005, when Dalal and Triggs have created the approach of a strong feature (Histogram of Oriented Gradient) and Support Vector Machine (SVM) framework for human detection. This paper proposes an approach to improve the salience of a weak feature descriptor by using intra-feature correlation. Although the intensity histogram distance feature known as Histogram Distance of Haar Regions (HDHR) itself is considered as a weak feature and can only be used to construct a weak learner to learn an AdaBoost classifier. In our paper, we explore the pairwise correlations between each and every histograms constructed and a strong feature can then be formulated. With the newly constructed strong feature based on histogram distances, a SVM classifier can be trained and later used for classification tasks. Promising experimental results have been obtained. © 2011 Springer-Verlag.

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http://hdl.handle.net/10453/31267