DocumentCode
1221117
Title
Mean shift is a bound optimization
Author
Fashing, Mark ; Tomasi, Carlo
Author_Institution
Dept. of Comput. Sci., Duke Univ., Durham, NC, USA
Volume
27
Issue
3
fYear
2005
fDate
3/1/2005 12:00:00 AM
Firstpage
471
Lastpage
474
Abstract
We build on the current understanding of mean shift as an optimization procedure. We demonstrate that, in the case of piecewise constant kernels, mean shift is equivalent to Newton´s method. Further, we prove that, for all kernels, the mean shift procedure is a quadratic bound maximization.
Keywords
Newton method; gradient methods; quadratic programming; Newton method; bound optimization; gradient methods; mean shift procedure; piecewise constant kernels; quadratic bound maximization; Application software; Computer vision; Convergence; Density functional theory; Image segmentation; Kernel; Optimization methods; Smoothing methods; Index Terms- Mean shift; Newton´s method; adaptive gradient descent; bound optimization; mode seeking.; Algorithms; Artificial Intelligence; Cluster Analysis; Computer Graphics; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Sample Size; Signal Processing, Computer-Assisted;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/TPAMI.2005.59
Filename
1388273
Link To Document