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dc.contributor.authorAlaei, Fahimeh
dc.contributor.authorAlaei, Alireza
dc.contributor.authorPal, Umapada
dc.contributor.authorBlumenstein, Michael
dc.date.accessioned2019-08-29T01:05:31Z
dc.date.available2019-08-29T01:05:31Z
dc.date.issued2019
dc.identifier.issn0957-4174
dc.identifier.doi10.1016/j.eswa.2018.12.007
dc.identifier.urihttp://hdl.handle.net/10072/386761
dc.description.abstractDue to the rapid increase of different digitised documents, there has been significant attention dedicated to document image retrieval over the past two decades. Finding discriminative and effective features is a fundamental task for providing a fast and more accurate retrieval system. Texture features are generally fast to compute and are suitable for large volume data. Thus, in this study, the effectiveness of texture features widely used in the literature of content-based image retrieval is investigated on document images. Twenty-six different texture feature extraction methods from four main categories of texture features, statistical, transform, model, and structural-based approaches, are considered in this research work to compare their performance on the problem of document image retrieval. Three document image datasets, MTDB, ITESOFT, and CLEF_IP with various content and page layouts are used to evaluate the twenty-six texture-based features on document image retrieval systems. The retrieval results are computed in terms of precision, recall and F-score, and a comparative analysis of the results is also provided. Feature dimensions and time complexity of the texture-based feature methods are further compared. Finally, some conclusions are drawn and suggestions are made about future research directions.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom97
dc.relation.ispartofpageto114
dc.relation.ispartofjournalExpert Systems with Applications
dc.relation.ispartofvolume121
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode01
dc.subject.fieldofresearchcode08
dc.subject.fieldofresearchcode09
dc.subject.keywordsScience & Technology
dc.subject.keywordsTechnology
dc.subject.keywordsComputer Science, Artificial Intelligence
dc.subject.keywordsEngineering, Electrical & Electronic
dc.subject.keywordsOperations Research & Management Science
dc.titleA comparative study of different texture features for document image retrieval
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationAlaei, F; Alaei, A; Pal, U; Blumenstein, M, A comparative study of different texture features for document image retrieval, Expert Systems with Applications, 2019, 121, pp. 97-114
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2019-08-29T00:59:34Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorAlaei, Fahimeh


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