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Concept
Mean shift
Résumé
Mean shift is a non-parametric feature-space mathematical analysis technique for locating the maxima of a density function, a so-called mode-seeking algorithm. Application domains include cluster analysis in computer vision and .
The mean shift procedure is usually credited to work by Fukunaga and Hostetler in 1975. It is, however, reminiscent of earlier work by Schnell in 1964.
Mean shift is a procedure for locating the maxima—the modes—of a density function given discrete data sampled from that function. This is an iterative method, and we start with an initial estimate . Let a kernel function be given. This function determines the weight of nearby points for re-estimation of the mean. Typically a Gaussian kernel on the distance to the current estimate is used, . The weighted mean of the density in the window determined by is
where is the neighborhood of , a set of points for which .
The difference is called mean shift in Fukunaga and Hostetler.
The mean-shift algorithm now sets , and repeats the estimation until converges.
Although the mean shift algorithm has been widely used in many applications, a rigid proof for the convergence of the algorithm using a general kernel in a high dimensional space is still not known. Aliyari Ghassabeh showed the convergence of the mean shift algorithm in one dimension with a differentiable, convex, and strictly decreasing profile function. However, the one-dimensional case has limited real world applications. Also, the convergence of the algorithm in higher dimensions with a finite number of the stationary (or isolated) points has been proved. However, sufficient conditions for a general kernel function to have finite stationary (or isolated) points have not been provided.
Gaussian Mean-Shift is an Expectation–maximization algorithm.
Let data be a finite set embedded in the -dimensional Euclidean space, . Let be a flat kernel that is the characteristic function of the -ball in ,
In each iteration of the algorithm, is performed for all simultaneously.
Source officielle
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