Home > Published Issues > 2014 > Volume 3, No. 3, July 2014 >

CADIST: COMPARATIVE ANALYSIS OF DIVERSE IMAGE SEGMENTATION TECHNIQUES

Mahantesh K, Monica M
Department of Electronic and Communication, SJBIT, Banglore 560060, India.

Abstract—Image segmentation is the initial step in image analysis and pattern recognition. Several generalpurpose algorithms and techniques have been developed for image segmentation. Since there is no general solution to the image segmentation problem, these techniques often have to be combined with domain knowledge in order to effectively solve an image segmentation problem for a problem domain. This paper presents a comparative study of the basic image segmentation techniques, i.e., corner based, fuzzy c-means, Region-Based and neural network techniques using a number of test images. We are planning to test each segmentation method over a representative set of input parameters that fully characterize algorithm performance over the complete image database. Experimental results have demonstrated that the proposed scheme could obtain promising segmentation results compared with other existing segmentation.

Index Terms—Keywords: Color space models, Fuzzy c-mean, Corner based, Region based method, Hybrid color space, Image segmentation, Neural network

Cite: Mahantesh K and Monica M, "CADIST: COMPARATIVE ANALYSIS OF DIVERSE IMAGE SEGMENTATION TECHNIQUES," International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 3, No. 3, pp. 21-28, July 2014.