Mean shift: A robust approach toward feature space analysis
Mean shift: A robust approach toward feature space analysis
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DOI：
10.1109/34.1000236
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发表时间：
2002-05-01
影响因子：
23.6
通讯作者：
Meer, P
中科院分区：
文献类型：
--
作者：
Comaniciu, D;Meer, P
本文提出了一种通用的非参数技术，用于分析复杂的多模态特征空间，并在其中描绘任意形状的聚类。该技术的基本计算模块是一个古老的模式识别过程，均值漂移。我们证明了离散数据的递归均值漂移过程的收敛到最近的平稳点的基本密度函数，因此，它的实用程序在检测模式的密度。建立了均值漂移过程与核回归的Nadaraya-Watson估计量和位置的稳健M-估计量之间的关系。两个低层次的视觉任务，不连续性保持平滑和图像分割的算法，作为应用程序。在这些算法中，唯一的用户设置参数是分析的分辨率，并且接受灰度或彩色图像作为输入。大量的实验结果证明了它们的优异性能。
A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators of location is also established. Algorithms for two low-level vision tasks, discontinuity preserving smoothing and image segmentation, are described as applications. In these algorithms, the only user set parameter is the resolution of the analysis and either gray level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.