代码 以下是一个使用NumPy和Matplotlib库的示例代码,用于Kernel Mean Matching(KMM),并使用示例数据集和图表。我将使用两个简单的一维数据集进行演示。 importnumpyasnpimportmatplotlib.pyplotasplt# 生成两个合成数据集np.random.seed(42)source_data=np.random.normal(0,1,100)target_data=np.random.normal(2,1,...
Mean-shift algorithm with fixed bandwidth often fails in tracking the object that moves with obviously change in scale,especially changing bigger.To solve the problem,a new adaptive bandwidth mean-shift tracking algorithm is proposed.The algorithm first matches the center of the tracking window with ...
Mean-shift 算法是一种基于核密度估计的算法,可以被认为是核密度估计的一个延伸。它同时也是一种聚类的算法,其思想可以概括为:确定密度函数的最大值。 这个方法的巧妙之处在于,由于其定义了核函数,相当于对每一个样本增加了权重系数,权重系数使得不同样本的权重不同,这就使得偏移值对偏移向量的“贡献”随着样本与...
Classic Mean-Shift based tracking algorithm uses fixed kernel-bandwidth, which limits the performance when the object scale exceeds the size of the tracking window. Based on the analysis of similarity of object kernel-histogram in different scales, i.e. the Bhattacharyya coefficient, a theorem is ...
RBF-SVM feature selection arithmeticbased on kernel space mean inter-class distance基于核空间类间平均距的径向基函数—支持向量机特征选择算法 HUANG Ying-qing,ZHAO Kai,JIANG Xiao-yu,黄应清,赵锴,蒋晓瑜 Keywords: support vector machine(SVM),feature selection,kernel function,radial basis function支持向量机...
Based on color histogram matching, Bhattacharyya coefficient is used to measure the similarity between template and possible target s model. 本文首先提出了一种有效的阴影去除算法检测运动目标,然后采用基于目标颜色直方图的相关匹配,使用Bhattacharyya系数度量目标模型与预测模型间的相似度,选出最优相似模型作为当前...
1. Gaussian kernel-based dynamic clustering algorithm 一类基于高斯核的动态聚类算法研究2. The Gaussian kernel mean-shift algorithm which is deduced from kernel density estimation has not been widely employed in applications because of its low convergence rate. 由核密度估计推导获得的高斯核均值漂移...