Fast fuzzy clustering of infrared images

Author

Eschrich, Steven ; Ke, Jingwei ; Hall, Lawrence O. ; Goldgof, Dmitry B.

Author_Institution

Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA

Volume

2

fYear

2001

fDate

25-28 July 2001

Firstpage

1145

Abstract

Clustering is an important technique for unsupervised image segmentation. The use of fuzzy c-means clustering can provide more information and better partitions than traditional c-means. In image processing, the ability to reduce the precision of the input data and aggregate similar examples can lead to significant data reduction and correspondingly less execution time. This paper discusses brFCM (bit reduction by Fuzzy C-Means), a data reduction fuzzy c-means clustering algorithm. The algorithm is described and several key implementation issues are discussed. Performance speedup and correspondence to a typical FCM implementation are presented from a data set of 172 infrared images. Average speedups of 59 times that of traditional FCM were obtained using brFCM, while producing identical cluster output relative to FCM

Keywords

data reduction; fuzzy set theory; image segmentation; infrared imaging; pattern clustering; software performance evaluation; unsupervised learning; IR images; bit reduction; brFCM algorithm; cluster output; data reduction; execution time; fuzzy c-means clustering; implementation issues; input data precision; partitions; performance speedup; similar examples aggregation; unsupervised image segmentation; Clustering algorithms; Computer science; Image generation; Image processing; Image segmentation; Infrared imaging; Layout; Partitioning algorithms; Quantization; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-7078-3

Type

conf

DOI

10.1109/NAFIPS.2001.944766

Filename

944766