A New Mean Shift Algorithm Based on Bacterial Colony Chemotaxis

Content Provider	Semantic Scholar
Author	Li, Yanling Li, Gang
Copyright Year	2012
Abstract	Mean shift is an effective statistical iterative algorithm. Like other gradient ascent optimization methods, it is susceptible to local maxima, and hence often fails to find the desired global maximum. And in the iterative process, size of bandwidth has great impact on the accuracy and efficiency of the algorithm. It not only decides the number of sampling points in the iteration, but also affects the convergence speed and accuracy of the algorithm. For the above reason, a new mean shift algorithm based on bacterial colony chemotaxis (BCC) is proposed in this paper. Firstly, the mean shift vector is optimized using BCC algorithm. Then, the optimal mean shift vector is updated using mean shift procedure. For the choice of bandwidth, bandwidth is calculated by BCC algorithm. Experimental results show that the proposed algorithm used for image segmentation can segment images more effectively and provide more robust segmentation results.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.ijfs.org.tw/ePublication/2012_paper_2/ijfs12-2-r-8-CSE0069-Manuscript_v1(5).pdf
Language	English
Access Restriction	Open
Subject Keyword	Algorithm Bicuculline Bricx Command Center Chemotaxis Ethanol 0.62 ML/ML Topical Gel Gradient descent Image segmentation Iteration Mathematical optimization Maxima and minima Maximum Mean shift Sampling (signal processing) Times Ascent biologic segmentation
Content Type	Text
Resource Type	Article