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.

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Xiaotong Yuan, Stan Z. Li
• Keywords: Adaptive Mean Shift, Algorithm Development, Arbitrary Starting Point, Computer Vision, ICCV 2007, Mean Shift, Mean Shift Procedure