广义高斯分布的分层Pitman-Yor过程混合模型的背景减法

Srikanth Amudala, Samr Ali, N. Bouguila
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引用次数: 0

摘要

本文提出了一种基于分层Pitman-Yor混合过程的广义高斯分布背景减法。选择广义高斯分布的动机是与广泛使用的高斯分布相比,它的灵活性。我们还将Pitman-Yor过程集成到我们提出的模型中,以实现无限扩展,从而在背景减法任务中获得更好的性能。我们的模型是通过变分贝叶斯方法学习的,并应用于具有挑战性的变化检测数据集。背景减法的实验结果表明了该算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Background Subtraction with a Hierarchical Pitman-Yor Process Mixture Model of Generalized Gaussian Distributions
This paper presents hierarchical Pitman-Yor process mixture of generalized Gaussian distributions for background subtraction. The motivation behind choosing generalized Gaussian distribution is its flexibility as compared to the widely used Gaussian. We also integrate the Pitman-Yor process into our proposed model for an infinite extension that leads to better performance in the task of background subtraction. Our model is learned via a variational Bayes approach and is applied on the challenging Change Detection dataset. Experimental results on background subtraction show the effectiveness of the proposed algorithm.
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