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dc.contributor.authorLin, C. -H.en_US
dc.contributor.authorLiu, C. -W.en_US
dc.contributor.authorChen, H. -Y.en_US
dc.date.accessioned2014-12-08T15:28:14Z-
dc.date.available2014-12-08T15:28:14Z-
dc.date.issued2012-10-01en_US
dc.identifier.issn1751-9659en_US
dc.identifier.urihttp://dx.doi.org/10.1049/iet-ipr.2011.0445en_US
dc.identifier.urihttp://hdl.handle.net/11536/20454-
dc.description.abstractIn this study, adaptive local binary patterns (ALBP) are proposed for image retrieval and classification. ALBP are based on texture features for local binary patterns. The texture features were used to propose an adaptive local binary patterns histogram (ALBPH) and gradient for adaptive local binary patterns (GALBP) in this study. Two texture features are most useful for describing the relationship in a local neighbourhood. ALBPH shows the texture distribution of an image by identifying and employing the difference between the centre pixel and the neighbourhood pixel values. In the GALBP, the gradient for each pixel is computed and the sum of the gradient of the ALBP number is adopted as an image feature. In this study, a set of colour and greyscale images were used to generate a variety of image subsets. Then, image retrieval and classification experiments were carried out for analysis and comparison with other methods. From the experimental results, the authors discovered that the proposed feature extraction method can effectively describe the characteristics of images in regard to texture image and image type. The image retrieval and classification experiments also produced better results than other methods.en_US
dc.language.isoen_USen_US
dc.titleImage retrieval and classification using adaptive local binary patterns based on texture featuresen_US
dc.typeArticleen_US
dc.identifier.doi10.1049/iet-ipr.2011.0445en_US
dc.identifier.journalIET IMAGE PROCESSINGen_US
dc.citation.volume6en_US
dc.citation.issue7en_US
dc.citation.spage822en_US
dc.citation.epage830en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000310054800002-
dc.citation.woscount5-
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