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Half Quadratic Analysis for Mean Shift: with Extension to A Sequential Data Mode-Seeking Method

9 years 7 months ago
Half Quadratic Analysis for Mean Shift: with Extension to A Sequential Data Mode-Seeking Method
Theoretical understanding and extension of mean shift procedure has received much attention recently [8, 18, 3]. In this paper, we present a theoretical exploration and an algorithm development on mean shift. In the theory part, we point out that convex profile based mean shift can be justified from the viewpoint of half-quadratic (HQ) optimization. Such analysis facilitates the convergence study and uni-mode bandwidth selection for the latest variation, annealed mean shift [18]. In the algorithm development part of this paper, we extend annealed mean shift inside our HQ framework to a novel method, namely adaptive mean shift (Ada-MS), to detect multiple data modes sequentially from an arbitrary starting point in linear running time. To validate the performance, we couple the investigation with two applications: image segmentation and color constancy. Extensive experiments show that the proposed method is time efficient and initialization invariant.
Xiaotong Yuan, Stan Z. Li
Added 14 Oct 2009
Updated 30 Oct 2009
Type Conference
Year 2007
Where ICCV
Authors Xiaotong Yuan, Stan Z. Li
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