On splitting dataset: Boosting Locally Adaptive Regression Kernels for car localization

Wang, S; Wu, Q; He, X; Xu, M

On splitting dataset: Boosting Locally Adaptive Regression Kernels for car localization

Wang, S Wu, Q

He, X

Xu, M

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Publication Type:: Conference Proceeding
Citation:: 2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012, 2012, pp. 1154 - 1159
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012, 2012, pp. 1154 - 1159	en_US
dc.identifier.isbn	9781467318716	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32327
dc.description.abstract	In this paper, we study the impact of learning an Adaboost classifier with small sample set (i.e., with fewer training examples). In particular, we make use of car localization as an underlying application, because car localization can be widely used to various real world applications. In order to evaluate the performance of Adaboost learning with a few examples, we simply apply Adaboost learning to a recently proposed feature descriptor - Locally Adaptive Regression Kernel (LARK). As a type of state-of-the-art feature descriptor, LARK is robust against illumination changes and noises. More importantly, we use LARK because its spatial property is also favorable for our purpose (i.e., each patch in the LARK descriptor corresponds to one unique pixel in the original image). In addition to learning a detector from the entire training dataset, we also split the original training dataset into several sub-groups and then we train one detector for each sub-group. We compare those features associated using the detector of each sub-group with that of the detector learnt with the entire training dataset and propose improvements based on the comparison results. Our experimental results indicate that the Adaboost learning is only successful on a small dataset when those learnt features simultaneously satisfy two conditions that: 1. features are learnt from the Region of Interest (ROI), and 2. features are sufficiently far away from each other. © 2012 IEEE.	en_US
dc.relation.ispartof	2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012	en_US
dc.relation.isbasedon	10.1109/ICARCV.2012.6485320	en_US
dc.title	On splitting dataset: Boosting Locally Adaptive Regression Kernels for car localization	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
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 - CRIN - Realtime Information Networks
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:

In this paper, we study the impact of learning an Adaboost classifier with small sample set (i.e., with fewer training examples). In particular, we make use of car localization as an underlying application, because car localization can be widely used to various real world applications. In order to evaluate the performance of Adaboost learning with a few examples, we simply apply Adaboost learning to a recently proposed feature descriptor - Locally Adaptive Regression Kernel (LARK). As a type of state-of-the-art feature descriptor, LARK is robust against illumination changes and noises. More importantly, we use LARK because its spatial property is also favorable for our purpose (i.e., each patch in the LARK descriptor corresponds to one unique pixel in the original image). In addition to learning a detector from the entire training dataset, we also split the original training dataset into several sub-groups and then we train one detector for each sub-group. We compare those features associated using the detector of each sub-group with that of the detector learnt with the entire training dataset and propose improvements based on the comparison results. Our experimental results indicate that the Adaboost learning is only successful on a small dataset when those learnt features simultaneously satisfy two conditions that: 1. features are learnt from the Region of Interest (ROI), and 2. features are sufficiently far away from each other. © 2012 IEEE.

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http://hdl.handle.net/10453/32327