A biological inspired features based saliency map

Han, B; Li, X; Gao, X; Tao, D

A biological inspired features based saliency map

Han, B Li, X Gao, X Tao, D

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Publication Type:: Conference Proceeding
Citation:: 2012 International Conference on Computing, Networking and Communications, ICNC'12, 2012, pp. 371 - 375
Issue Date:: 2012-04-24

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Field	Value	Language
dc.contributor.author	Han, B	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2012-04-24	en_US
dc.identifier.citation	2012 International Conference on Computing, Networking and Communications, ICNC'12, 2012, pp. 371 - 375	en_US
dc.identifier.isbn	9781467300094	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121185
dc.description.abstract	A visual attention mechanism is believed to be responsible for the most informative spots in complex scenes. We proposed a novel biologically inspired attention model based on Cortex-like mechanisms and sparse representation. Biological Inspired model, HMAX model, is a feature extraction method and this method is motivated by a quantitative model of visual cortex. This biological inspired feature will be used to build the Saliency Criteria to measure the perspective fields. Saliency Criteria is obtained from Shannon's information entropy and sparse representation. We demonstrate that the proposed model achieves superior accuracy with the comparison to classical approach in static saliency map generation on real data of natural scenes and psychology stimuli patterns. © 2012 IEEE.	en_US
dc.relation.ispartof	2012 International Conference on Computing, Networking and Communications, ICNC'12	en_US
dc.relation.isbasedon	10.1109/ICCNC.2012.6167446	en_US
dc.title	A biological inspired features based saliency map	en_US
dc.type	Conference Proceeding
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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

A visual attention mechanism is believed to be responsible for the most informative spots in complex scenes. We proposed a novel biologically inspired attention model based on Cortex-like mechanisms and sparse representation. Biological Inspired model, HMAX model, is a feature extraction method and this method is motivated by a quantitative model of visual cortex. This biological inspired feature will be used to build the Saliency Criteria to measure the perspective fields. Saliency Criteria is obtained from Shannon's information entropy and sparse representation. We demonstrate that the proposed model achieves superior accuracy with the comparison to classical approach in static saliency map generation on real data of natural scenes and psychology stimuli patterns. © 2012 IEEE.

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http://hdl.handle.net/10453/121185