Local coding based matching kernel method for image classification.

This paper mainly focuses on how to effectively and efficiently measure visual similarity for local feature based representation. Among existing methods, metrics based on Bag of Visual Word (BoV) techniques are efficient and conceptually simple, at the expense of effectiveness. By contrast, kernel b...

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Autores principales:	Yan Song, Ian Vince McLoughlin, Li-Rong Dai
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/067323e021fc4b56985e3b8a63b118f4
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spelling	oai:doaj.org-article:067323e021fc4b56985e3b8a63b118f42021-11-25T06:04:50ZLocal coding based matching kernel method for image classification.1932-620310.1371/journal.pone.0103575https://doaj.org/article/067323e021fc4b56985e3b8a63b118f42014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25119982/?tool=EBIhttps://doaj.org/toc/1932-6203This paper mainly focuses on how to effectively and efficiently measure visual similarity for local feature based representation. Among existing methods, metrics based on Bag of Visual Word (BoV) techniques are efficient and conceptually simple, at the expense of effectiveness. By contrast, kernel based metrics are more effective, but at the cost of greater computational complexity and increased storage requirements. We show that a unified visual matching framework can be developed to encompass both BoV and kernel based metrics, in which local kernel plays an important role between feature pairs or between features and their reconstruction. Generally, local kernels are defined using Euclidean distance or its derivatives, based either explicitly or implicitly on an assumption of Gaussian noise. However, local features such as SIFT and HoG often follow a heavy-tailed distribution which tends to undermine the motivation behind Euclidean metrics. Motivated by recent advances in feature coding techniques, a novel efficient local coding based matching kernel (LCMK) method is proposed. This exploits the manifold structures in Hilbert space derived from local kernels. The proposed method combines advantages of both BoV and kernel based metrics, and achieves a linear computational complexity. This enables efficient and scalable visual matching to be performed on large scale image sets. To evaluate the effectiveness of the proposed LCMK method, we conduct extensive experiments with widely used benchmark datasets, including 15-Scenes, Caltech101/256, PASCAL VOC 2007 and 2011 datasets. Experimental results confirm the effectiveness of the relatively efficient LCMK method.Yan SongIan Vince McLoughlinLi-Rong DaiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 8, p e103575 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yan Song Ian Vince McLoughlin Li-Rong Dai Local coding based matching kernel method for image classification.
description	This paper mainly focuses on how to effectively and efficiently measure visual similarity for local feature based representation. Among existing methods, metrics based on Bag of Visual Word (BoV) techniques are efficient and conceptually simple, at the expense of effectiveness. By contrast, kernel based metrics are more effective, but at the cost of greater computational complexity and increased storage requirements. We show that a unified visual matching framework can be developed to encompass both BoV and kernel based metrics, in which local kernel plays an important role between feature pairs or between features and their reconstruction. Generally, local kernels are defined using Euclidean distance or its derivatives, based either explicitly or implicitly on an assumption of Gaussian noise. However, local features such as SIFT and HoG often follow a heavy-tailed distribution which tends to undermine the motivation behind Euclidean metrics. Motivated by recent advances in feature coding techniques, a novel efficient local coding based matching kernel (LCMK) method is proposed. This exploits the manifold structures in Hilbert space derived from local kernels. The proposed method combines advantages of both BoV and kernel based metrics, and achieves a linear computational complexity. This enables efficient and scalable visual matching to be performed on large scale image sets. To evaluate the effectiveness of the proposed LCMK method, we conduct extensive experiments with widely used benchmark datasets, including 15-Scenes, Caltech101/256, PASCAL VOC 2007 and 2011 datasets. Experimental results confirm the effectiveness of the relatively efficient LCMK method.
format	article
author	Yan Song Ian Vince McLoughlin Li-Rong Dai
author_facet	Yan Song Ian Vince McLoughlin Li-Rong Dai
author_sort	Yan Song
title	Local coding based matching kernel method for image classification.
title_short	Local coding based matching kernel method for image classification.
title_full	Local coding based matching kernel method for image classification.
title_fullStr	Local coding based matching kernel method for image classification.
title_full_unstemmed	Local coding based matching kernel method for image classification.
title_sort	local coding based matching kernel method for image classification.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/067323e021fc4b56985e3b8a63b118f4
work_keys_str_mv	AT yansong localcodingbasedmatchingkernelmethodforimageclassification AT ianvincemcloughlin localcodingbasedmatchingkernelmethodforimageclassification AT lirongdai localcodingbasedmatchingkernelmethodforimageclassification
_version_	1718414203500363776

Local coding based matching kernel method for image classification.

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