ANT COLONY SYSTEM WITH MEDIAN BASED PARTITIONINGFOR IMAGE SEGMENTATION AND CLASSIFICATION

Abstract

The motivation we address in this paper is to find out a generic method used to
segment and classify different types of conceptual images. A novel median based
method was proposed as primary stage for image segmentation, in which the image is
partitioned into fixed sized quadrants called kernels. The size of kernels in a specific
image is determined according to the spectral uniformity measurements. Later, Ant
Colony Optimization (ACO) is used to find out the optimal number of classes may exist
in the image, and then classify the image in terms of the determined classes. Different
types of images with different semantic concepts were used to test the proposed
classification method. The results obtained by ACP ensure the success of the proposed
method and the effective performance of classification.