基于规范化的B样条密度模型的聚类算法

针对有参混合模型的聚类算法需要假设模型为某种已知的参数模型, 存在模型不匹配及非参数正交多项式密度估计不是概率密度函数的问题, 提出了一种基于规范化的B样条密度模型的图像聚类算法。通过构建基于规范化的B样条密度函数的非参数混合模型, 利用非参数B样条期望最大(NNBEM: Non parametric B splineExpectation Maximum)算法估计密度模型的未知参数, 并根据贝叶斯准则实现图像的聚类。该方法不需要对模型做任何假设, 可有效克服有参混合模型与实际数据分布不一致问题。对模拟图像和真实图像数据进行仿真的结果表明, 规范化的B样条密度模型的聚类算法比其他算法具有更好的聚类性能。

Clustering Algorithm Based on Normalized B-Spline Density Model

Parametric mixture models for clustering algorithm depend too much on the prior assumptions and the orthogonal series density estimator is not aprobability density function. To overcome these problems, a new image clustering algorithm based on normalized B-spline density model is proposed. A non-parametric mixture models based on normalized B-spline density function is designed, and NNBEM (Non-parametric B-spline Expectation Maximum) algorithm is used to
estimate the unknown parameter of the density model, and the image clustering is in accordance with the Bayesian criterion. This algorithm dose not require any prior assumptions on the model, and it can effectively overcome the problem of the inconsistency between the parametric mixture models and the actual distribution. Some experiments about artificial data and real images are tested. These results show that the clustering method based on normalized B-spline density model is better than other algorithms.