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dc.contributor.authorShakerdonyavi, M.
dc.contributor.authorShanbehzadeh, J.
dc.contributor.authorSarrafzadeh, Hossein
dc.contributor.editor-
dc.date.accessioned2017-07-05T03:07:48Z
dc.date.available2017-07-05T03:07:48Z
dc.date.issued2015-11
dc.identifier.urihttps://hdl.handle.net/10652/3835
dc.description.abstractHashing algorithm is an efficient approximate searching algorithm for large-scale image retrieval. Learning binary code is a key step to improve its performance and it is still an ongoing challenge. The inputs of Hashing affects its performance. This paper proposes a method to improve the efficiency of learning binary code by improving the suitableness of the Hashing algorithms inputs by employing local binary patterns in extracting image features. This approach results in more compact code, less memory and computational requirement and higher performance. The reasons behind these achievements are the binary nature and high efficiency in feature generation of local binary pattern. The performance analysis consists of using CIFAR-10 and precision vs. recall rate as dataset and evaluation criteria respectively. The simulations compare the new algorithm with three state of the art and along the line algorithms from three points of view; the hashing code size, memory space and computational cost, and the results demonstrate the effectiveness of the new approach.en_NZ
dc.language.isoenen_NZ
dc.relation.urihttp://ieeexplore.ieee.org/document/7371276/en_NZ
dc.subjectbinary codesen_NZ
dc.subjectcontent-based retrievalen_NZ
dc.subjectimage retrievalen_NZ
dc.subjectimage feature extractionen_NZ
dc.subjectlarge scale content based image retrieval (LSCBIR)en_NZ
dc.subjecthashing algorithmen_NZ
dc.titleLarge-scale image retrieval using local binary patterns and iterative quantizationen_NZ
dc.typeConference Contribution - Paper in Published Proceedingsen_NZ
dc.date.updated2017-05-10T05:38:08Z
dc.rights.holderAuthorsen_NZ
dc.identifier.doi10.1109/DICTA.2015.7371276en_NZ
dc.subject.marsden080109 Pattern Recognition and Data Miningen_NZ
dc.identifier.bibliographicCitationShakerdonyavi, M., Shanbehzadeh, J., & Sarrafzadeh, A. (2015, November). Large-Scale Image Retrieval Using Local Binary Patterns and Iterative Quantization. - (Ed.), International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp.1-5). 10.1109/DICTA.2015.7371276.en_NZ
unitec.publication.spage1en_NZ
unitec.publication.lpage5en_NZ
unitec.conference.titleInternational Conference on Digital Image Computing: Techniques and Applications (DICTA 2015)en_NZ
unitec.conference.orgAustralian Department of Defenceen_NZ
unitec.conference.orgFlinders University (Adelaide, South Australia)en_NZ
unitec.conference.sdate2015-11-23
unitec.conference.edate2015-11-25
unitec.peerreviewedyesen_NZ
dc.contributor.affiliationUnitec Institute of Technologyen_NZ
dc.contributor.affiliationUniversity of Kharazmi (Tehran, Iran)en_NZ
unitec.identifier.roms59859en_NZ
unitec.institution.studyareaComputing


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