Feature selection for low bit rate mobile augmented reality applications

Cao, Y; Ritz, C; Raad, R

Feature selection for low bit rate mobile augmented reality applications

Cao, Y

Ritz, C Raad, R

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Publication Type:: Journal Article
Citation:: Signal Processing: Image Communication, 2015, 36 pp. 115 - 126
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Cao, Y https://orcid.org/0000-0002-7623-9830	en_US
dc.contributor.author	Ritz, C	en_US
dc.contributor.author	Raad, R	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Signal Processing: Image Communication, 2015, 36 pp. 115 - 126	en_US
dc.identifier.issn	0923-5965	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119440
dc.description.abstract	© 2015 Elsevier B.V. Abstract Mobile augmented reality applications rely on automatically matching a captured visual scene to an image in a database. This is typically achieved by deriving a set of features from the captured image, transmitting them through a network and then matching with features derived from a database of reference images. A fundamental problem is to select as few and robust features as possible such that the matching accuracy is invariant to distortions caused by camera capture whilst minimizing the bit rate required for their transmission. In this paper, novel feature selection methods are proposed, based on the entropy of the image content, entropy of extracted features and the Discrete Cosine Transformation (DCT) coefficients. The methods proposed in the descriptor domain and DCT domain achieve better retrieval accuracy under low bit rate transmission than state-of-the-art peak based feature selection used within the MPEG-7 Compact Descriptor for Visual Search (CDVS). The robustness of the proposed methods is evaluated under controlled single distortion and the retrieval performance is verified from image retrieval experiments and results for a realistic dataset with complex real world capturing distortion. Results show that the proposed method can improve the matching accuracy for various detectors. On the one side, feature selection can achieve low bit rate transmission, on the other side, when coping with distorted images, it can result in a higher matching accuracy than using all features. Hence, even if all the features can be transmitted in high transmission bandwidth scenarios, feature selection should still be applied to the distorted query image to ensure high matching accuracy.	en_US
dc.relation.ispartof	Signal Processing: Image Communication	en_US
dc.relation.isbasedon	10.1016/j.image.2015.06.008	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Feature selection for low bit rate mobile augmented reality applications	en_US
dc.type	Journal Article
utslib.citation.volume	36	en_US
utslib.for	0906 Electrical and Electronic Engineering	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
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	36	en_US

Abstract:

© 2015 Elsevier B.V. Abstract Mobile augmented reality applications rely on automatically matching a captured visual scene to an image in a database. This is typically achieved by deriving a set of features from the captured image, transmitting them through a network and then matching with features derived from a database of reference images. A fundamental problem is to select as few and robust features as possible such that the matching accuracy is invariant to distortions caused by camera capture whilst minimizing the bit rate required for their transmission. In this paper, novel feature selection methods are proposed, based on the entropy of the image content, entropy of extracted features and the Discrete Cosine Transformation (DCT) coefficients. The methods proposed in the descriptor domain and DCT domain achieve better retrieval accuracy under low bit rate transmission than state-of-the-art peak based feature selection used within the MPEG-7 Compact Descriptor for Visual Search (CDVS). The robustness of the proposed methods is evaluated under controlled single distortion and the retrieval performance is verified from image retrieval experiments and results for a realistic dataset with complex real world capturing distortion. Results show that the proposed method can improve the matching accuracy for various detectors. On the one side, feature selection can achieve low bit rate transmission, on the other side, when coping with distorted images, it can result in a higher matching accuracy than using all features. Hence, even if all the features can be transmitted in high transmission bandwidth scenarios, feature selection should still be applied to the distorted query image to ensure high matching accuracy.

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http://hdl.handle.net/10453/119440