BMC@MediaEval 2017 multimedia satellite task via regression random forest

Fu, X; Bin, Y; Peng, L; Zhou, J; Yang, Y; Shen, HT

BMC@MediaEval 2017 multimedia satellite task via regression random forest

Fu, X Bin, Y Peng, L Zhou, J Yang, Y Shen, HT

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Publication Type:: Conference Proceeding
Citation:: CEUR Workshop Proceedings, 2017, 1984
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Fu, X	en_US
dc.contributor.author	Bin, Y	en_US
dc.contributor.author	Peng, L	en_US
dc.contributor.author	Zhou, J	en_US
dc.contributor.author	Yang, Y	en_US
dc.contributor.author	Shen, HT	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	CEUR Workshop Proceedings, 2017, 1984	en_US
dc.identifier.issn	1613-0073	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126669
dc.description.abstract	© 2017 Author/owner(s). In the MediaEval 2017 Multimedia Satellite Task, we propose an approach based on regression random forest which can extract valuable information from a few images and their corresponding metadata. The experimental results show that when processing social media images, the proposed method can be high-performance in circumstances where the images features are low-level and the training samples are relatively small of number.Additionally,when the low-level color features of satellite images are too ambiguous to analyze, random forest is also a efiective way to detect flooding area.	en_US
dc.relation.ispartof	CEUR Workshop Proceedings	en_US
dc.title	BMC@MediaEval 2017 multimedia satellite task via regression random forest	en_US
dc.type	Conference Proceeding
utslib.citation.volume	1984	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 Software
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	1984	en_US

Abstract:

© 2017 Author/owner(s). In the MediaEval 2017 Multimedia Satellite Task, we propose an approach based on regression random forest which can extract valuable information from a few images and their corresponding metadata. The experimental results show that when processing social media images, the proposed method can be high-performance in circumstances where the images features are low-level and the training samples are relatively small of number.Additionally,when the low-level color features of satellite images are too ambiguous to analyze, random forest is also a efiective way to detect flooding area.

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

http://hdl.handle.net/10453/126669