Multinomial Latent Logistic Regression for Image Understanding

Xu, Z; Hong, Z; Zhang, Y; Wu, J; Tsoi, AC; Tao, D

Multinomial Latent Logistic Regression for Image Understanding

Xu, Z Hong, Z Zhang, Y Wu, J Tsoi, AC Tao, D

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2016, 25 (2), pp. 973 - 987
Issue Date:: 2016-02-01

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Field	Value	Language
dc.contributor.author	Xu, Z	en_US
dc.contributor.author	Hong, Z	en_US
dc.contributor.author	Zhang, Y	en_US
dc.contributor.author	Wu, J	en_US
dc.contributor.author	Tsoi, AC	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2016-02-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2016, 25 (2), pp. 973 - 987	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122721
dc.description.abstract	© 1992-2012 IEEE. In this paper, we present multinomial latent logistic regression (MLLR), a new learning paradigm that introduces latent variables to logistic regression. By inheriting the advantages of logistic regression, MLLR is efficiently optimized using the second-order derivatives and provides effective probabilistic analysis on output predictions. MLLR is particularly effective in weakly supervised settings where the latent variable has an exponential number of possible values. The effectiveness of MLLR is demonstrated on four different image understanding applications, including a new challenging architectural style classification task. Furthermore, we show that MLLR can be generalized to general structured output prediction, and in doing so, we provide a thorough investigation of the connections and differences between MLLR and existing related algorithms, including latent structural SVMs and hidden conditional random fields.	en_US
dc.relation	http://purl.org/au-research/grants/arc/FT130101457
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2015.2509422	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Multinomial Latent Logistic Regression for Image Understanding	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	25	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	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.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	25	en_US

Abstract:

© 1992-2012 IEEE. In this paper, we present multinomial latent logistic regression (MLLR), a new learning paradigm that introduces latent variables to logistic regression. By inheriting the advantages of logistic regression, MLLR is efficiently optimized using the second-order derivatives and provides effective probabilistic analysis on output predictions. MLLR is particularly effective in weakly supervised settings where the latent variable has an exponential number of possible values. The effectiveness of MLLR is demonstrated on four different image understanding applications, including a new challenging architectural style classification task. Furthermore, we show that MLLR can be generalized to general structured output prediction, and in doing so, we provide a thorough investigation of the connections and differences between MLLR and existing related algorithms, including latent structural SVMs and hidden conditional random fields.

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http://hdl.handle.net/10453/122721