Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/36504
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dc.contributor.authorTsai, M.H.en_US
dc.contributor.author吳俊霖zh_TW
dc.contributor.authorChan, Y.K.en_US
dc.contributor.authorWang, J.S.en_US
dc.contributor.authorGuo, S.W.en_US
dc.contributor.authorWu, J.L.en_US
dc.contributor.author蔡孟勳zh_TW
dc.contributor.author詹永寬zh_TW
dc.date2009zh_TW
dc.date.accessioned2014-06-06T07:56:49Z-
dc.date.available2014-06-06T07:56:49Z-
dc.identifier.issn1024-123Xzh_TW
dc.identifier.urihttp://hdl.handle.net/11455/36504-
dc.description.abstractThe techniques of K-means algorithm and Gaussian Markov random field model are integrated to provide a Gaussian Markov random field model (GMRFM) feature which can describe the texture information of different pixel colors in an image. Based on this feature, an image retrieval method is also provided to seek the database images most similar to a given query image. In this paper, a genetic-based parameter detector is presented to decide the fittest parameters used by the proposed image retrieval method, as well. The experimental results manifested that the image retrieval method is insensitive to the rotation, translation, distortion, noise, scale, hue, light, and contrast variations, especially distortion, hue, and contrast variations. Copyright (C) 2009 Meng-Hsiun Tsai et al.en_US
dc.language.isoen_USzh_TW
dc.relationMathematical Problems in Engineeringen_US
dc.relation.ispartofseriesMathematical Problems in Engineering.en_US
dc.relation.urihttp://dx.doi.org/10.1155/2009/410243en_US
dc.subjectgenetic algorithmen_US
dc.subjectclassificationen_US
dc.subjectsimilarityen_US
dc.titleColor-Texture-Based Image Retrieval System Using Gaussian Markov Random Field Modelen_US
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1155/2009/410243zh_TW
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