On the Convergence of the Mean Shift Algorithm in the One-Dimensional Space
This provides a theoretical foundation for the mean shift algorithm, which is incremental as it addresses a specific gap in existing literature for applications in machine vision and pattern recognition.
The paper tackles the lack of a rigorous proof for the convergence of the mean shift algorithm by proving that in one-dimensional space, the sequence generated by the algorithm is monotone and convergent.
The mean shift algorithm is a non-parametric and iterative technique that has been used for finding modes of an estimated probability density function. It has been successfully employed in many applications in specific areas of machine vision, pattern recognition, and image processing. Although the mean shift algorithm has been used in many applications, a rigorous proof of its convergence is still missing in the literature. In this paper we address the convergence of the mean shift algorithm in the one-dimensional space and prove that the sequence generated by the mean shift algorithm is a monotone and convergent sequence.