本論文利用核支持向量機擷取運動影片的精采片段與分類紡織品的等級。核支持向量機包含訓練模組與分析模組，首先以訓練模組產生分類函數，再利用分析模組對分析資料進行分類，進而產生分類結果。不同於原本的方法，本論文使用基因演算法優化核支援向量機的參數和選取特徵值，以達到較佳的準確率。
擷取影片精采片段的方法是採用影像和聲音的特徵值進行訓練，再將影像和聲音的特徵值配合核支持向量機分析，以得到影片的精采片段。擷取精采片段方法的特點在於不需要偵測精采片段中常出現的物件與預先定義精采片段之發生順序，即可擷取影片精彩片段。
分類紡織品等級的方法是採用毛球的特徵值進行訓練，再將毛球的特徵值配合核支持向量機分析，以得到紡織品的等級優劣。紡織品等級分類方法的特點在於不需要利用人眼判斷毛球的數量，即可辨別紡織品的等級。
擷取不同運動影片精采片段的平均準確率可達81%，而分類紡織品等級的平均準確率可達83%。由本篇論文之實驗結果可知，透過核支持向量機能有效的擷取影片精采片段並且分類紡織品的等級。

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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