DocumentCode
3249416
Title
A genetic clustering algorithm guided by a descent algorithm
Author
Scott, G.P. ; Clark, D.I. ; Pham, Tuan
Author_Institution
Canberra Univ., ACT, USA
Volume
2
fYear
2001
fDate
2001
Firstpage
734
Abstract
This paper considers a clustering problem where distorted images are allocated into a specific number of clusters such that each cluster is composed of images that are similar. Similarity is determined by summing the mean squared error of each image of a cluster with the centroid of that cluster. A genetic algorithm guided by a descent algorithm is presented to minimise this error. Tabu search is also employed to maintain genetic diversity and improve efficiency. Experiments use monochrome, greyscale and colour images
Keywords
genetic algorithms; gradient methods; image recognition; pattern clustering; search problems; centroid; clustering problem; colour images; descent algorithm; distorted images; genetic algorithm; genetic clustering algorithm; greyscale images; mean squared error; monochrome images; tabu search; Australia; Biological cells; Biomedical imaging; Clustering algorithms; Euclidean distance; Genetic algorithms; Genetic mutations; Nonlinear distortion; Pattern recognition; Simulated annealing;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2001. Proceedings of the 2001 Congress on
Conference_Location
Seoul
Print_ISBN
0-7803-6657-3
Type
conf
DOI
10.1109/CEC.2001.934262
Filename
934262
Link To Document