Adaptive decision support system for on-line multi-class learning and object detection

Hong, GJ; Li, DL; Pare, S; Saxena, A; Prasad, M; Lin, CT

Adaptive decision support system for on-line multi-class learning and object detection

Hong, GJ Li, DL Pare, S Saxena, A Prasad, M

Lin, CT

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Applied Sciences (Switzerland), 2021, 11, (23)
Issue Date:: 2021-12-01

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Field	Value	Language
dc.contributor.author	Hong, GJ
dc.contributor.author	Li, DL
dc.contributor.author	Pare, S
dc.contributor.author	Saxena, A
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.contributor.author	Lin, CT
dc.date.accessioned	2022-03-09T04:56:24Z
dc.date.available	2022-03-09T04:56:24Z
dc.date.issued	2021-12-01
dc.identifier.citation	Applied Sciences (Switzerland), 2021, 11, (23)
dc.identifier.issn	1454-5101
dc.identifier.issn	2076-3417
dc.identifier.uri	http://hdl.handle.net/10453/155100
dc.description.abstract	A new online multi-class learning algorithm is proposed with three main characteristics. First, in order to make the feature pool fitter for the pattern pool, the adaptive feature pool is proposed to dynamically combine the three general features, Haar-like, Histogram of Oriented Gradient (HOG), and Local Binary Patterns (LBP). Second, the external model is integrated into the proposed model without re-training to enhance the efficacy of the model. Third, a new multi-class learning and updating mechanism are proposed that help to find unsuitable decisions and adjust them automatically. The performance of the proposed model is validated with multi-class detection and online learning system. The proposed model achieves a better score than other non-deep learning algorithms used in public pedestrian and multi-class databases. The multi-class databases contain data for pedestrians, faces, vehicles, motorcycles, bicycles, and aircraft.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Applied Sciences (Switzerland)
dc.relation.isbasedon	10.3390/app112311268
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Adaptive decision support system for on-line multi-class learning and object detection
dc.type	Journal Article
utslib.citation.volume	11
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2022-03-09T04:56:19Z
pubs.issue	23
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	23

Abstract:

A new online multi-class learning algorithm is proposed with three main characteristics. First, in order to make the feature pool fitter for the pattern pool, the adaptive feature pool is proposed to dynamically combine the three general features, Haar-like, Histogram of Oriented Gradient (HOG), and Local Binary Patterns (LBP). Second, the external model is integrated into the proposed model without re-training to enhance the efficacy of the model. Third, a new multi-class learning and updating mechanism are proposed that help to find unsuitable decisions and adjust them automatically. The performance of the proposed model is validated with multi-class detection and online learning system. The proposed model achieves a better score than other non-deep learning algorithms used in public pedestrian and multi-class databases. The multi-class databases contain data for pedestrians, faces, vehicles, motorcycles, bicycles, and aircraft.

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http://hdl.handle.net/10453/155100