| Summary: | 碩士 === 國立成功大學 === 電機工程學系碩博士班 === 98 === We use a classification method based on Kernel support vector machines (Kernel SVM), that can be applied to various types of data. We use Kernel SVM to extract the video highlights of sport and classify textile grade. Different form original classification method, we optimize the parameters and the features by Genetic Algorithm. The Kernel SVM is composed of the training mode and the analysis mode. In the training mode, we adopt the Kernel SVM to train classification function. In the analysis mode, we use the classification function to generate the classification result. We use the video and audio features without predefining any highlight rule of the events. The precision of highlight extraction by Kernel SVM can achieve about 81%, while that of textile grade classification is approximately 83% The experimental results show the proposed method can extract video highlights of sport, and it can also be applied to textile grade classification.
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