Discovering local cooccurring patterns from aerial images

Jia, W; Tien, D

Discovering local cooccurring patterns from aerial images

Jia, W

Tien, D

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Publication Type:: Conference Proceeding
Citation:: Fourth International Conference on Information Technology and Applications, ICITA 2007, 2007
Issue Date:: 2007-12-03

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Field	Value	Language
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.contributor.author	Tien, D	en_US
dc.date.issued	2007-12-03	en_US
dc.identifier.citation	Fourth International Conference on Information Technology and Applications, ICITA 2007, 2007	en_US
dc.identifier.isbn	0980326702	en_US
dc.identifier.isbn	9780980326703	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10799
dc.description.abstract	Developing a spatial searching engine to enhance the search capabilities of large spatial repositories for GIS update has attracted more and more attention. Existing methods are usually designed to extract limited types of objects and use only one aspect of features of Images. In this paper, we propose to use the local cooccurring patterns to disclose the cooccurring relationships among each dominant local features and use this local cooccurring patterns to recognize an object from aerial images. For this purpose, we investigate three types of local features: colour-based features, texture-based features, and edgebased shape features. In order to facilitate the feature extraction procedure, we first use discontinuity-preserving smoothing methods to filter the input image. Two popular smoothing techniques are tested and compared. Experimental results are presented in this paper.	en_US
dc.relation.ispartof	Fourth International Conference on Information Technology and Applications, ICITA 2007	en_US
dc.title	Discovering local cooccurring patterns from aerial images	en_US
dc.type	Conference Proceeding
utslib.for	080106 Image Processing	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Harbin, China	en_US
dc.location.activity	Auckland, New Zealand
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
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

Developing a spatial searching engine to enhance the search capabilities of large spatial repositories for GIS update has attracted more and more attention. Existing methods are usually designed to extract limited types of objects and use only one aspect of features of Images. In this paper, we propose to use the local cooccurring patterns to disclose the cooccurring relationships among each dominant local features and use this local cooccurring patterns to recognize an object from aerial images. For this purpose, we investigate three types of local features: colour-based features, texture-based features, and edgebased shape features. In order to facilitate the feature extraction procedure, we first use discontinuity-preserving smoothing methods to filter the input image. Two popular smoothing techniques are tested and compared. Experimental results are presented in this paper.

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http://hdl.handle.net/10453/10799