A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE)is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN)classifier in formation. The class conditional de...A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE)is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN)classifier in formation. The class conditional density is estimated by KDE and the bandwidthof the kernel function is estimated by Expectation Maximum (EM) algorithm. Two subspaceanalysis methods-linear Principal Component Analysis (PCA) and Kernel-based PCA (KPCA)are respectively used to extract features, and the proposed method is compared with ProbabilisticReasoning Models (PRM), Nearest Center (NC) and NN classifiers which are widely used in facerecognition systems. The experiments are performed on two benchmarks and the experimentalresults show that the KDE outperforms PRM, NC and NN classifiers.展开更多
音频分类是音频信号处理中一项重要的预处理工作。该文描述了一种基于能量的分类方法,将音频信号分为语音和音乐2种类型。分类的过程分为3个阶段,首先计算优化低能量率MLER(modified low energy ratio)作为特征,然后利用初级分类器得到...音频分类是音频信号处理中一项重要的预处理工作。该文描述了一种基于能量的分类方法,将音频信号分为语音和音乐2种类型。分类的过程分为3个阶段,首先计算优化低能量率MLER(modified low energy ratio)作为特征,然后利用初级分类器得到初步分类的结果,最后利用音频类别的前后相关性,使用上下文分类器修正初始分类得到最终分类的结果。该文重点对MLER中参数的合理选取范围进行了讨论,并对传统的初始分类器作了改进,用非参数分类器和参数分类器代替原有的Bayes硬判决的方法,避免了由于门限选择不当所带来的分类错误。实验表明,使用参数分类器时,对纯语音和纯音乐分类效果很好,正确率达99%以上。展开更多
基金National "863" project (2001AA114140) the National Natural Science Foundation of China (60135020).
文摘A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE)is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN)classifier in formation. The class conditional density is estimated by KDE and the bandwidthof the kernel function is estimated by Expectation Maximum (EM) algorithm. Two subspaceanalysis methods-linear Principal Component Analysis (PCA) and Kernel-based PCA (KPCA)are respectively used to extract features, and the proposed method is compared with ProbabilisticReasoning Models (PRM), Nearest Center (NC) and NN classifiers which are widely used in facerecognition systems. The experiments are performed on two benchmarks and the experimentalresults show that the KDE outperforms PRM, NC and NN classifiers.
文摘音频分类是音频信号处理中一项重要的预处理工作。该文描述了一种基于能量的分类方法,将音频信号分为语音和音乐2种类型。分类的过程分为3个阶段,首先计算优化低能量率MLER(modified low energy ratio)作为特征,然后利用初级分类器得到初步分类的结果,最后利用音频类别的前后相关性,使用上下文分类器修正初始分类得到最终分类的结果。该文重点对MLER中参数的合理选取范围进行了讨论,并对传统的初始分类器作了改进,用非参数分类器和参数分类器代替原有的Bayes硬判决的方法,避免了由于门限选择不当所带来的分类错误。实验表明,使用参数分类器时,对纯语音和纯音乐分类效果很好,正确率达99%以上。
