Using genetic algorithm in image clustering

Abstract EN

The aim of this work is to optimize gray scale image clustering using two traditional methods, these are thresholding technique and genetic algorithm (GA).

The clustering optimization is achieved by applying three features (gray value, distance, gray connection) based thresholding technique and genetic algorithm.

In this work clustering optimization includes segmenting the image to find regions that represent objects or meaningful parts of objects depending on the above mentioned three features which base on gray value of image and two standard mathematical theories these are chessboard distance and breshenham's algorithm.

There are many recent researches in this subject some of them depending on gray value feature to clustering images, but in this research depended on three features which is making the clustering operation more accuracy.