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Retrieval of colour and texture images using local directional peak valley binary pattern

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dc.contributor.authorGupta, Srishti-
dc.contributor.authorRoy, Partha Pratim-
dc.contributor.authorDogra, Debi Prosad-
dc.contributor.authorKim, Byung-Gyu-
dc.date.available2021-02-22T05:21:26Z-
dc.date.issued2020-11-
dc.identifier.issn1433-7541-
dc.identifier.issn1433-755X-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/1084-
dc.description.abstractMany content-based image retrieval (CBIR) methods are being developed to store more and more information about images in shorter feature vectors and to improve image retrieval rate. In the proposed method, two-step approach to CBIR has been developed. The first step generates an image mask from local binary pattern (LBP). This LBP mask is then utilized to draw comparison between the centre pixel and the eight surrounding pixels. The second step involves drawing the peak and valley patterns of local directional binary pattern for each image which is then combined with the colour histogram to retrieve similar images. Existing methods suffer from lower average image retrieval accuracy even with larger feature vectors. The proposed method overcomes such problems through shorter feature vectors that can store more information about the image. As illustrated through experimental results, the proposed method produces promising results with shorter feature vector of length 56 and improved image retrieval rate of about 5-10%. Our method outperforms similar techniques when tested with public data sets.-
dc.format.extent17-
dc.language영어-
dc.language.isoENG-
dc.publisherSPRINGER-
dc.titleRetrieval of colour and texture images using local directional peak valley binary pattern-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1007/s10044-020-00879-4-
dc.identifier.scopusid2-s2.0-85084135731-
dc.identifier.wosid000527475800001-
dc.identifier.bibliographicCitationPATTERN ANALYSIS AND APPLICATIONS, v.23, no.4, pp 1569 - 1585-
dc.citation.titlePATTERN ANALYSIS AND APPLICATIONS-
dc.citation.volume23-
dc.citation.number4-
dc.citation.startPage1569-
dc.citation.endPage1585-
dc.type.docTypeArticle; Early Access-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.subject.keywordPlusMULTIRESOLUTION GRAY-SCALE-
dc.subject.keywordPlusCOOCCURRENCE PATTERN-
dc.subject.keywordPlusFEATURE DESCRIPTOR-
dc.subject.keywordPlusFACE-
dc.subject.keywordPlusROTATION-
dc.subject.keywordPlusCLASSIFICATION-
dc.subject.keywordAuthorLBP value-
dc.subject.keywordAuthorImage retrieval-
dc.subject.keywordAuthorImage classification-
dc.subject.keywordAuthorFeature vector-
dc.subject.keywordAuthorTexture database-
dc.subject.keywordAuthorFace database-
dc.subject.keywordAuthorLocal binary pattern-
dc.identifier.urlhttps://link.springer.com/article/10.1007%2Fs10044-020-00879-4-
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