A connectionist approach for clustering with applications in image analysis

Author

Vinod, V.V. ; Chaudhury, Santanu ; Mukherjee, Jayanta ; Ghose, Sujoy

Author_Institution

Dept. of Comput. Sci. & Eng., Indian Inst. of Technol., Kharagpur, India

Volume

24

Issue

3

fYear

1994

fDate

3/1/1994 12:00:00 AM

Firstpage

365

Lastpage

384

Abstract

A new neural network strategy for clustering is presented. The network works on the histogram and the process is similar to mode separation. The number of clusters are autonomously detected by the network and it overcomes some major difficulties encountered by mode separation techniques. Clustering is done by first selecting the prototypes and then assigning patterns to one of the prototypes based on its distance from the prototype and the distribution of data. The network does not employ weight learning and is therefore faster than existing unsupervised learning networks. The network was applied to a wide class of problems including gray level image reduction, color segmentation and remotely sensed image segmentation. The experimental results obtained are promising

Keywords

image segmentation; neural nets; clustering; color segmentation; connectionist approach; gray level image reduction; histogram; image analysis; mode separation; neural network strategy; remotely sensed image segmentation; unsupervised learning networks; Clustering algorithms; Clustering methods; Histograms; Image analysis; Image color analysis; Image segmentation; Intelligent networks; Neural networks; Prototypes; Unsupervised learning;

fLanguage

English

Journal_Title

Systems, Man and Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9472

Type

jour

DOI

10.1109/21.278988

Filename

278988