Robust Perceptual Image Hashing Based on Ring Partition and NMF

Tang, Z; Zhang, X; Zhang, S

Robust Perceptual Image Hashing Based on Ring Partition and NMF

Tang, Z Zhang, X Zhang, S

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Publisher:: IEEE
Publication Type:: Journal Article
Citation:: IEEE Transactions On Knowledge And Data Engineering, 2014, 2013 pp. 1 - 14
Issue Date:: 2014-01

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Field	Value	Language
dc.contributor.author	Tang, Z	en_US
dc.contributor.author	Zhang, X	en_US
dc.contributor.author	Zhang, S	en_US
dc.date.issued	2014-01	en_US
dc.identifier.citation	IEEE Transactions On Knowledge And Data Engineering, 2014, 2013 pp. 1 - 14	en_US
dc.identifier.issn	1041-4347	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26317
dc.description.abstract	This paper designs an efficient image hashing with a ring partition and a non-negative matrix factorization (NMF), which is with both the rotation robustness and good discriminative capability. The key contribution is a novel construction of rotation-invariant secondary image, which is used for the first time in image hashing and is helpful to make image hash resistant to rotation. In addition, NMF coefficients are approximately linearly changed by content-preserving manipulations, so as to measure hash similarity with correlation coefficient. We conduct experiments for illustrating the efficiency with 346 images. Our experiments show that the proposed hashing is robust against content-preserving operations, such as image rotation, JPEG compression, watermark embedding, Gaussian low-pass filtering, gamma correction, brightness adjustment, contrast adjustment and image scaling. Receiver operating characteristics (ROC) curve comparisons are also conducted with the state-of-the-art algorithms, and demonstrate that the proposed hashing is much better than all these algorithms in classification performances with respect to robustness and discrimination.	en_US
dc.format	c1	en_US
dc.publisher	IEEE	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0985456
dc.relation.ispartof	IEEE Transactions On Knowledge And Data Engineering	en_US
dc.relation.isbasedon	10.1109/TKDE.2013.45	en_US
dc.rights	© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Robust Perceptual Image Hashing Based on Ring Partition and NMF	en_US
dc.type	Journal Article
utslib.citation.volume	2013	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing 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.consider-herdc	true	en_US
pubs.volume	2013	en_US

Abstract:

This paper designs an efficient image hashing with a ring partition and a non-negative matrix factorization (NMF), which is with both the rotation robustness and good discriminative capability. The key contribution is a novel construction of rotation-invariant secondary image, which is used for the first time in image hashing and is helpful to make image hash resistant to rotation. In addition, NMF coefficients are approximately linearly changed by content-preserving manipulations, so as to measure hash similarity with correlation coefficient. We conduct experiments for illustrating the efficiency with 346 images. Our experiments show that the proposed hashing is robust against content-preserving operations, such as image rotation, JPEG compression, watermark embedding, Gaussian low-pass filtering, gamma correction, brightness adjustment, contrast adjustment and image scaling. Receiver operating characteristics (ROC) curve comparisons are also conducted with the state-of-the-art algorithms, and demonstrate that the proposed hashing is much better than all these algorithms in classification performances with respect to robustness and discrimination.

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http://hdl.handle.net/10453/26317