Object detection based on co-occurrence GMuLBP features

Xu, J; Wu, Q; Zhang, J; Tang, Z

Object detection based on co-occurrence GMuLBP features

Xu, J Wu, Q

Zhang, J

Tang, Z

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Multimedia and Expo, 2012, pp. 943 - 948
Issue Date:: 2012-11-05

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Field	Value	Language
dc.contributor.author	Xu, J	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	Tang, Z	en_US
dc.date.issued	2012-11-05	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Multimedia and Expo, 2012, pp. 943 - 948	en_US
dc.identifier.issn	1945-7871	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31914
dc.description.abstract	Image co-occurrence has shown great powers on object classification because it captures the characteristic of individual features and spatial relationship between them simultaneously. For example, Co-occurrence Histogram of Oriented Gradients (CoHOG) has achieved great success on human detection task. However, the gradient orientation in CoHOG is sensitive to noise. In addition, CoHOG does not take gradient magnitude into account which is a key component to reinforce the feature detection. In this paper, we propose a new LBP feature detector based image co-occurrence. Building on uniform Local Binary Patterns, the new feature detector detects Co-occurrence Orientation through Gradient Magnitude calculation. It is known as CoGMuLBP. An extension version of the GoGMuLBP is also presented. The experimental results on the UIUC car data set show that the proposed features outperform state-of-the-art methods. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Multimedia and Expo	en_US
dc.relation.isbasedon	10.1109/ICME.2012.41	en_US
dc.title	Object detection based on co-occurrence GMuLBP features	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	2012 IEEE International Conference on Multimedia and Expo
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 - 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:

Image co-occurrence has shown great powers on object classification because it captures the characteristic of individual features and spatial relationship between them simultaneously. For example, Co-occurrence Histogram of Oriented Gradients (CoHOG) has achieved great success on human detection task. However, the gradient orientation in CoHOG is sensitive to noise. In addition, CoHOG does not take gradient magnitude into account which is a key component to reinforce the feature detection. In this paper, we propose a new LBP feature detector based image co-occurrence. Building on uniform Local Binary Patterns, the new feature detector detects Co-occurrence Orientation through Gradient Magnitude calculation. It is known as CoGMuLBP. An extension version of the GoGMuLBP is also presented. The experimental results on the UIUC car data set show that the proposed features outperform state-of-the-art methods. © 2012 IEEE.

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http://hdl.handle.net/10453/31914