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Mean Shift Is a Bound Optimization
March 2005 (vol. 27 no. 3)
pp. 471-474
ASCII Text
x 
 
Mark Fashing, Carlo Tomasi, "Mean Shift Is a Bound Optimization," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 471-474, March, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2005.59,
author = {Mark Fashing and Carlo Tomasi},
title = {Mean Shift Is a Bound Optimization},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {27},
number = {3},
issn = {0162-8828},
year = {2005},
pages = {471-474},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.59},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Mean Shift Is a Bound Optimization
IS - 3
SN - 0162-8828
SP471
EP474
EPD - 471-474
A1 - Mark Fashing,
A1 - Carlo Tomasi,
PY - 2005
KW - Mean shift
KW - bound optimization
KW - Newton's method
KW - adaptive gradient descent
KW - mode seeking.
VL - 27
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
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.

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Index Terms:
Mean shift, bound optimization, Newton's method, adaptive gradient descent, mode seeking.
Citation:
Mark Fashing, Carlo Tomasi, "Mean Shift Is a Bound Optimization," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 471-474, March 2005, doi:10.1109/TPAMI.2005.59
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