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Published Articles >> Table of Contents >> Abstract
18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)
pp. 583-590
An Approximation to Mean-Shift via Swarm Intelligence
M. Thomas, University of Delaware, USA
C. Kambhamettu, University of Delaware, USA
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICTAI.2006.30
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| Abstract |
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Mean shift based feature space analysis has been shown
to be an elegant, accurate and robust technique. The elegance
in this non-parametric algorithm is mainly due to
its simplicity in performing gradient ascent to estimate the
modes in a multidimensional data. One characteristic aspect
of mean shift is that the mode estimation is performed
at each data point. Since it is important to describe the data
in as succinct manner as possible, it is important to focus on
modal points in the data instead of every data point. In this
paper, we attempt to tackle the mean shift problem through a
"mode centric" approach using swarm intelligence. Here,
the mode estimation is cast as a problem of goal seeking for
the swarm as it moves through the multidimensional data
space. Local maxima/minima and plateaus are avoided
through information exchange between each member of the
swarm, thereby converging at the mode values efficiently.
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 Additional Information
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Citation:
M. Thomas, C. Kambhamettu,
"An Approximation to Mean-Shift via Swarm Intelligence,"
ictai,
pp. 583-590,
18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06),
2006
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