On the Convergence and Consistency of the Blurring Mean-Shift Process
This provides theoretical guarantees for a variant of the mean-shift algorithm used in computer vision and image processing, but it is incremental as it builds on existing methods.
The paper tackles the problem of analyzing the blurring mean-shift algorithm, proving its convergence and consistency, and shows through simulations that it is more efficient than the nonblurring version.
The mean-shift algorithm is a popular algorithm in computer vision and image processing. It can also be cast as a minimum gamma-divergence estimation. In this paper we focus on the "blurring" mean shift algorithm, which is one version of the mean-shift process that successively blurs the dataset. The analysis of the blurring mean-shift is relatively more complicated compared to the nonblurring version, yet the algorithm convergence and the estimation consistency have not been well studied in the literature. In this paper we prove both the convergence and the consistency of the blurring mean-shift. We also perform simulation studies to compare the efficiency of the blurring and the nonblurring versions of the mean-shift algorithms. Our results show that the blurring mean-shift has more efficiency.