University Links: Home Page | Site Map
Covenant University Repository

Pixel Intensity Clustering Algorithm for Multilevel Image Segmentation

Olugbara, O. O. and Adetiba, E. and Oyewole, Stanley A. (2015) Pixel Intensity Clustering Algorithm for Multilevel Image Segmentation. Mathematical Problems in Engineering. pp. 1-19.

[img] PDF
Download (13Mb)

Abstract

Image segmentation is an important problem that has received significant attention in the literature. Over the last few decades, a lot of algorithms were developed to solve image segmentation problem; prominent amongst these are the thresholding algorithms. However, the computational time complexity of thresholding exponentially increases with increasing number of desired thresholds. A wealth of alternative algorithms, notably those based on particle swarm optimization and evolutionary metaheuristics, were proposed to tackle the intrinsic challenges of thresholding. In codicil, clustering based algorithms were developed as multidimensional extensions of thresholding. While these algorithms have demonstrated successful results for fewer thresholds, their computational costs for a large number of thresholds are still a limiting factor. We propose a new clustering algorithm based on linear partitioning of the pixel intensity set and between-cluster variance criterion function for multilevel image segmentation. The results of testing the proposed algorithm on real images from Berkeley Segmentation Dataset and Benchmark show that the algorithm is comparable with state-of-the-art multilevel segmentation algorithms and consistently produces high quality results. The attractive properties of the algorithm are its simplicity, generalization to a large number of clusters, and computational cost effectiveness.

Item Type: Article
Subjects: T Technology > T Technology (General)
T Technology > TC Hydraulic engineering. Ocean engineering
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Mrs Hannah Akinwumi
Date Deposited: 13 Sep 2016 14:13
Last Modified: 13 Sep 2016 14:13
URI: http://eprints.covenantuniversity.edu.ng/id/eprint/7162

Actions (login required)

View Item View Item