DocumentCode
419475
Title
The mean shift algorithm and the unified framework
Author
Abrantes, Arnaldo J. ; Marques, Jorge S.
Author_Institution
ISEL, Lisboa, Portugal
Volume
1
fYear
2004
fDate
23-26 Aug. 2004
Firstpage
244
Abstract
This paper considers two classes of algorithms for the representation of data points using centroids: the unified framework and the mean shift algorithm. The relationship between both approaches is presented showing that the mean shift algorithm fits within the unified framework being equivalent to snake with Cohen potential. However it does not use competitive learning as the other methods considered in the unified framework. The advantages of both types of techniques are exemplified through examples.
Keywords
data structures; feature extraction; fuzzy set theory; minimisation; pattern clustering; Cohen potential; competitive learning; data point representation; feature extraction; fuzzy set theory; mean shift algorithm; minimisation; pattern clustering; unified framework; Density functional theory; Equations; Image analysis; Image segmentation; Indexing; Kernel; Pattern recognition; Probability density function; Prototypes; Video coding;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
ISSN
1051-4651
Print_ISBN
0-7695-2128-2
Type
conf
DOI
10.1109/ICPR.2004.1334069
Filename
1334069
Link To Document