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
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;
Conference_Titel :
Evolutionary Computation, 2001. Proceedings of the 2001 Congress on
Conference_Location :
Seoul
Print_ISBN :
0-7803-6657-3
DOI :
10.1109/CEC.2001.934262
