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dc.contributor.authorLing, Hefei
dc.contributor.authorZou, Fuhao
dc.contributor.authorYan, Wei-Qi
dc.contributor.authorMa, Qingzhen
dc.contributor.authorCheng, Hongrui
dc.date.accessioned2011-08-19T20:50:37Z
dc.date.accessioned2011-08-20T22:29:16Z
dc.date.available2011-08-19T20:50:37Z
dc.date.available2011-08-20T22:29:16Z
dc.date.copyright2011
dc.date.issued2011-08-20
dc.identifier.citationIEEE MultiMedia Magazine. January-March 2012 (vol. 19 no. 1) pp. 60-69.
dc.identifier.issn1070-986X
dc.identifier.urihttp://hdl.handle.net/10292/1774
dc.descriptionInspired by multi-resolution histogram, we propose a multi-scale SIFT descriptor to improve the discriminability. A series of SIFT descriptions with different scale are first acquired by varying the actual size of each spatial bin. Then principle component analysis (PCA) is employed to reduce them to low dimensional vectors, which are further combined into one 128-dimension multi-scale SIFT description. Next, an entropy maximization based binarization is employed to encode the descriptions into binary codes called fingerprints for indexing the local features. Furthermore, an efficient search architecture consisting of lookup tables and inverted image ID list is designed to improve the query speed. Since the fingerprint building is of low-complexity, this method is very efficient and scalable to very large databases. In addition, the multi-scale fingerprints are very discriminative such that the copies can be effectively distinguished from similar objects, which leads to an improved performance in the detection of copies. The experimental evaluation shows that our approach outperforms the state of the art methods.
dc.description.abstractInspired by multi-resolution histogram, we propose a multi-scale SIFT descriptor to improve the discriminability. A series of SIFT descriptions with different scale are first acquired by varying the actual size of each spatial bin. Then principle component analysis (PCA) is employed to reduce them to low dimensional vectors, which are further combined into one 128-dimension multi-scale SIFT description. Next, an entropy maximization based binarization is employed to encode the descriptions into binary codes called fingerprints for indexing the local features. Furthermore, an efficient search architecture consisting of lookup tables and inverted image ID list is designed to improve the query speed. Since the fingerprint building is of low-complexity, this method is very efficient and scalable to very large databases. In addition, the multi-scale fingerprints are very discriminative such that the copies can be effectively distinguished from similar objects, which leads to an improved performance in the detection of copies. The experimental evaluation shows that our approach outperforms the state of the art methods.
dc.publisherIEEE
dc.relation.replaceshttp://hdl.handle.net/10292/1773
dc.relation.replaces10292/1773
dc.rights© 2011 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.
dc.subjectCopy detection
dc.subjectFngerprints
dc.subjectMulti-scale SIFT descriptor
dc.subjectVisual words
dc.subjectHistogram intersection
dc.titleEfficient image copy detection using multi-scale fingerprints
dc.typeJournal Article
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.1109/MMUL.2011.75


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