Multi-instance learning with an extended kernel density estimation for object categorization

Du, R; Wu, Q; He, X; Yang, J

Multi-instance learning with an extended kernel density estimation for object categorization

Du, R Wu, Q

He, X

Yang, J

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2012, 2012, pp. 477 - 482
Issue Date:: 2012-10-04

Closed Access

	Filename	Description	Size
	2012004345OK.pdf		511.82 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Du, R	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	Yang, J	en_US
dc.date.issued	2012-10-04	en_US
dc.identifier.citation	Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2012, 2012, pp. 477 - 482	en_US
dc.identifier.isbn	9780769547299	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22953
dc.description.abstract	Multi-instance learning (MIL) is a variational supervised learning. Instead of getting a set of instances that are labeled, the learner receives a set of bags that are labeled. Each bag contains many instances. In this paper, we present a novel MIL algorithm that can efficiently learn classifiers in a large instance space. We achieve this by estimating instance distribution using a proposed extended kernel density estimation (eKDE) which is an alternative to previous diverse density estimation (DDE). A fast method is devised to approximately locate the multiple modes of eKDE. Comparing to DDE, eKDE is more efficient and robust to the labeling noise (the mislabeled training data). We compare our approach with other state-of-the-art MIL methods in object categorization on the popular Caltech-4 and SIVAL datasets, the results illustrate that our approach provides superior performance. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2012	en_US
dc.relation.isbasedon	10.1109/ICMEW.2012.89	en_US
dc.title	Multi-instance learning with an extended kernel density estimation for object categorization	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Melbourne, Australia	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:

Multi-instance learning (MIL) is a variational supervised learning. Instead of getting a set of instances that are labeled, the learner receives a set of bags that are labeled. Each bag contains many instances. In this paper, we present a novel MIL algorithm that can efficiently learn classifiers in a large instance space. We achieve this by estimating instance distribution using a proposed extended kernel density estimation (eKDE) which is an alternative to previous diverse density estimation (DDE). A fast method is devised to approximately locate the multiple modes of eKDE. Comparing to DDE, eKDE is more efficient and robust to the labeling noise (the mislabeled training data). We compare our approach with other state-of-the-art MIL methods in object categorization on the popular Caltech-4 and SIVAL datasets, the results illustrate that our approach provides superior performance. © 2012 IEEE.

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

http://hdl.handle.net/10453/22953