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dc.contributor.authorLiu, Lei
dc.contributor.authorBai, Xiao
dc.contributor.authorZhang, Huigang
dc.contributor.authorZhou, Jun
dc.contributor.authorTang, Wenzhong
dc.date.accessioned2017-05-22T06:12:01Z
dc.date.available2017-05-22T06:12:01Z
dc.date.issued2016
dc.identifier.issn0925-2312
dc.identifier.doi10.1016/j.neucom.2014.12.120
dc.identifier.urihttp://hdl.handle.net/10072/142474
dc.description.abstractIn this paper, we propose a novel latent structural model for big data image recognition. It addresses the problem that large amount of labeled training samples are needed in traditional structural models. This method first builds an initial structural model by using only one labeled image. After pooling unlabeled samples into the initial model, an incremental learning process is used to find more candidate parts and to update the model. The appearance features of the parts are described by multiple kernel learning method that assembles more information of the parts, such as color, edge, and texture. Therefore, the proposed model considers not only independent components but also their inherent spatial and appearance relationships. Finally, the updated model is applied to recognition tasks. Experiments show that this method is effective in handling big data problems and has achieved better performance than several state-of-the-art methods.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom355
dc.relation.ispartofpageto363
dc.relation.ispartofissuePart 2
dc.relation.ispartofjournalNeurocomputing
dc.relation.ispartofvolume173
dc.subject.fieldofresearchInformation and Computing Sciences not elsewhere classified
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchPsychology and Cognitive Sciences
dc.subject.fieldofresearchcode089999
dc.subject.fieldofresearchcode08
dc.subject.fieldofresearchcode09
dc.subject.fieldofresearchcode17
dc.titleDescribing and learning of related parts based on latent structural model in big data
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2016 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorZhou, Jun


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