Data clustering using hierarchical deterministic annealing and higher order statistics [image processing]

Author

Rajagopalan, A.N. ; Jain, Avinash ; Desai, U.B.

Author_Institution

Dept. of Electr. Eng., Indian Inst. of Technol., Bombay, India

Volume

46

Issue

8

fYear

1999

fDate

8/1/1999 12:00:00 AM

Firstpage

1100

Lastpage

1104

Abstract

In this brief, we propose an extension to the hierarchical deterministic annealing (HDA) algorithm for clustering by incorporating additional features into the algorithm. To decide a split in a cluster, the interdependency among all the clusters is taken into account by using the entire data distribution. A general distortion measure derived from the higher order statistics (HOS) of the data is used to analyze the phase transitions. Experimental results clearly demonstrate the improvement in the performance of the HDA algorithm when the interdependency among the clusters and the HOS of the data points are also utilized for the purpose of clustering

Keywords

data compression; higher order statistics; image classification; image coding; image segmentation; data clustering; data distribution; distortion measure; hierarchical deterministic annealing; higher order statistics; image compression; image segmentation; interdependency; phase transitions; Annealing; Clustering algorithms; Clustering methods; Distortion measurement; Higher order statistics; Image processing; Image segmentation; Phase distortion; Phase measurement; Temperature;

fLanguage

English

Journal_Title

Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7130

Type

jour

DOI

10.1109/82.782060

Filename

782060