Mixture models are broadly applied in image processing domains. Related existing challenges include failure to approximate exact data shapes, estimate correct number of components, and ignore irrelevant features. In this study, the authors develop a statistical self-refinement framework for the background subtraction task by using Dirichlet Process-based asymmetric Gaussian mixture model. The parameters of this model are learned using variational inference methods. They also incorporate feature selection simultaneously within the framework to avoid noisy influence from uninformative features. To validate the proposed framework, they report their results on background subtraction tasks on 8 different datasets for infrared and visible videos.

中文翻译：

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使用无限非对称高斯混合模型同时进行特征选择进行背景扣除

混合模型广泛应用于图像处理领域。相关的现有挑战包括无法近似精确的数据形状，估计正确的组件数量以及忽略不相关的功能。在这项研究中，作者使用基于Dirichlet Process的非对称高斯混合模型，为背景扣除任务开发了一个统计自优化框架。使用变分推断方法学习该模型的参数。它们还同时在框架内合并了特征选择，以避免来自非信息特征的噪声影响。为了验证所提出的框架，他们报告了有关红外和可见视频的8个不同数据集的背景扣除任务的结果。