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Then various-order statistics of the textures within a sliding two-dimensional window are measured. K-mean algorithm is used to initialise the clustering procedure by labelling part of the class members and the classifier parameters. Therefore at this stage we have both the training and the working sets. A non-linear S<jats:sup>3<\/jats:sup>VM is then developed to exploit both sets to classify all the regions. The convex algorithm maximises a defined cost function by incorporating a number of constraints. The algorithm has been applied to combinations of a number of natural textures. It is demonstrated that the algorithm is robust, with negligible misclassification error. However, for complex textures there may be a minor misplacement of the edges.<\/jats:p>","DOI":"10.1142\/s1469026804001197","type":"journal-article","created":{"date-parts":[[2004,9,17]],"date-time":"2004-09-17T10:44:54Z","timestamp":1095417894000},"page":"131-142","source":"Crossref","is-referenced-by-count":2,"title":["TEXTURE SEGMENTATION USING SEMI-SUPERVISED SUPPORT VECTOR MACHINES"],"prefix":"10.1142","volume":"04","author":[{"given":"SAEID","family":"SANEI","sequence":"first","affiliation":[{"name":"Centre for Digital Signal Processing Research, Department of Electronic Engineering, King's College London, London, WC2R 2LS, United Kingdom"}]}],"member":"219","published-online":{"date-parts":[[2011,11,20]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1142\/9789814343138_0010"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1973.4309314"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1109\/34.761261"},{"key":"rf4","volume":"27","author":"Zhu S. 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