ANT COLONY SYSTEM WITH MEDIAN BASED PARTITIONING FOR IMAGE SEGMENTATION AND CLASSIFICATION

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.