Support Vector Machine Learning by Robinson Jonathan (15 results)
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Kartoniert / Broschiert. Condition: New. Methods exploring the application of support vectormachine learning (SVM) to still image compression aredetailed in both the spatial and frequency domains.In particular the sparse properties of SVM learningare exploited in the compre.
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Taschenbuch. Condition: Neu. Support Vector Machine Learning | Application to Compression of Digital Images | Jonathan Robinson | Taschenbuch | Kartoniert / Broschiert | Englisch | 2008 | VDM Verlag Dr. Müller | EAN 9783639100006 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken,…info[at]akademikerverlag[dot]de | Anbieter: preigu.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Methods exploring the application of support vectormachine learning (SVM) to still image compression aredetailed in both the spatial and frequency domains.In particular the sparse properties of SVM learningare exploited in the compressio…n algorithms. Aclassic radial basis function neural network requiresthat the topology of the network be defined beforetraining. An SVM has the property that it will choosethe minimum number of training points to use ascentres of the Gaussian kernel functions. It is thisproperty that is exploited as the basis for imagecompression algorithms presented in this book.Several novel algorithms are developed applying SVMlearning to both directly model the colour surfaceand model transform coefficients after the surfacehas been transformed into the frequency domain. It isdemonstrated that compression is more efficient infrequency space.In the frequency domain, results are superior to thatof JPEG. For example, the quality of the industrystandard Lena image compressed 63:1 for JPEG isslightly worse quality than the same image compressed192:1 with the RKi-1 algorithm detailed in this book.
