This paper presents a new online incremental training algorithm of Gaussian mixture model （GMM）, which aims to perform the expectation-maximization（EM） training incrementally to update GMM model parameters online ...This paper presents a new online incremental training algorithm of Gaussian mixture model （GMM）, which aims to perform the expectation-maximization（EM） training incrementally to update GMM model parameters online sample by sample, instead of waiting for a block of data with the sufficient size to start training as in the traditional EM procedure. The proposed method is extended from the split-and-merge EM procedure, so inherently it is also capable escaping from local maxima and reducing the chances of singularities. In the application domain, the algorithm is optimized in the context of speech processing applications. Experiments on the synthetic data show the advantage and efficiency of the new method and the results in a speech processing task also confirm the improvement of system performance.展开更多
传统的马尔科夫随机场(Markov random field,MRF)图像分割算法易受初始化的影响,并且其迭代求解方法易受噪声影响而难以得到准确的结果。针对以上问题,首先利用基于区域的K-Means算法进行初始化,以提高算法的稳定性和准确性。其次,对基...传统的马尔科夫随机场(Markov random field,MRF)图像分割算法易受初始化的影响,并且其迭代求解方法易受噪声影响而难以得到准确的结果。针对以上问题,首先利用基于区域的K-Means算法进行初始化,以提高算法的稳定性和准确性。其次,对基于区域的MRF图像分割进行了建模,并将全局最优合并(global best merge,GBM)与MRF图像分割相结合,以进一步提高分割精度。为了提高GBM的实现效率,采用了一种高级数据结构——红黑树,来实现GBM的搜索过程。利用模拟图像开展了分割实验,结果显示,提出的算法可以有效提高遥感影像的分割精度。展开更多
文摘This paper presents a new online incremental training algorithm of Gaussian mixture model （GMM）, which aims to perform the expectation-maximization（EM） training incrementally to update GMM model parameters online sample by sample, instead of waiting for a block of data with the sufficient size to start training as in the traditional EM procedure. The proposed method is extended from the split-and-merge EM procedure, so inherently it is also capable escaping from local maxima and reducing the chances of singularities. In the application domain, the algorithm is optimized in the context of speech processing applications. Experiments on the synthetic data show the advantage and efficiency of the new method and the results in a speech processing task also confirm the improvement of system performance.
文摘传统的马尔科夫随机场(Markov random field,MRF)图像分割算法易受初始化的影响,并且其迭代求解方法易受噪声影响而难以得到准确的结果。针对以上问题,首先利用基于区域的K-Means算法进行初始化,以提高算法的稳定性和准确性。其次,对基于区域的MRF图像分割进行了建模,并将全局最优合并(global best merge,GBM)与MRF图像分割相结合,以进一步提高分割精度。为了提高GBM的实现效率,采用了一种高级数据结构——红黑树,来实现GBM的搜索过程。利用模拟图像开展了分割实验,结果显示,提出的算法可以有效提高遥感影像的分割精度。
