An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift ...An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift （AMS） based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling （GMM） of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.展开更多
本文研究了一种结合"声学信息"和"音素配位学信息"进行语言辨识的新算法,首先在预处理中对语音进行自动分段,在特征层上引入带有长时信息的段级特征参数——段级移位差分倒谱,在模型层上利用高斯混合模型(Gaussi- a...本文研究了一种结合"声学信息"和"音素配位学信息"进行语言辨识的新算法,首先在预处理中对语音进行自动分段,在特征层上引入带有长时信息的段级特征参数——段级移位差分倒谱,在模型层上利用高斯混合模型(Gaussi- an Mixture Model,GMM)将语音信号自动标识为符号序列,进而引入多元语言模型(Multi-gram Language Model,MLM)来对"音素配位学信息"进行建模,最后将"GMM得分"和"MLM得分"送入后端多分类支持向量机模型得到最终识别结果。相关实验表明,新系统不需手工标识的语料,识别速度快,对OGI标准语料库中的五种语言获得了开集正识率为78.84%的结果。展开更多
文摘An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift （AMS） based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling （GMM） of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.
文摘本文研究了一种结合"声学信息"和"音素配位学信息"进行语言辨识的新算法,首先在预处理中对语音进行自动分段,在特征层上引入带有长时信息的段级特征参数——段级移位差分倒谱,在模型层上利用高斯混合模型(Gaussi- an Mixture Model,GMM)将语音信号自动标识为符号序列,进而引入多元语言模型(Multi-gram Language Model,MLM)来对"音素配位学信息"进行建模,最后将"GMM得分"和"MLM得分"送入后端多分类支持向量机模型得到最终识别结果。相关实验表明,新系统不需手工标识的语料,识别速度快,对OGI标准语料库中的五种语言获得了开集正识率为78.84%的结果。
