Automated two-dimensional K-means clustering algorithm for unsupervised image segmentation

Authors:
Intan Aidha Yusoff;Nor Ashidi Mat Isa;Khairunnisa Hasikin
Affiliations:
Imaging and Intelligent System Research Team (ISRT), School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia;Imaging and Intelligent System Research Team (ISRT), School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia;Imaging and Intelligent System Research Team (ISRT), School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia
Venue:
Computers and Electrical Engineering
Year:
2013

Citing 16
Cited 1

Quantitative evaluation of color image segmentation results

Pattern Recognition Letters
Multiresolution Color Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Image Segmentation Using Automated Fuzzy c-Means

CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
Automated strawberry grading system based on image processing

Computers and Electronics in Agriculture
Medical Image Segmentation Using Improved Mountain Clustering Technique Version-2

ITNG '10 Proceedings of the 2010 Seventh International Conference on Information Technology: New Generations
Comparison of Four Kinds of Fuzzy C-Means Clustering Methods

ISIP '10 Proceedings of the 2010 Third International Symposium on Information Processing
A Cluster Separation Measure

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust clustering methods: a unified view

IEEE Transactions on Fuzzy Systems
Red-eye detection and correction using inpainting in digital photographs

IEEE Transactions on Consumer Electronics
Color shift model-based image enhancement for digital multifocusing based on a multiple color-filter aperture camera

IEEE Transactions on Consumer Electronics
Object-based multilevel contrast stretching method for image enhancement

IEEE Transactions on Consumer Electronics
Adaptive fuzzy-K-means clustering algorithm for image segmentation

IEEE Transactions on Consumer Electronics
Local tone mapping using the K-means algorithm and automatic gamma setting

IEEE Transactions on Consumer Electronics
Automated global enhancement of digitized photographs

IEEE Transactions on Consumer Electronics
A recursive thresholding technique for image segmentation

IEEE Transactions on Image Processing

The learning system of collective behavior in students' social network

Computers and Electrical Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces the Automated Two-Dimensional K-Means (A2DKM) algorithm, a novel unsupervised clustering technique. The proposed technique differs from the conventional clustering techniques because it eliminates the need for users to determine the number of clusters. In addition, A2DKM incorporates local and spatial information of the data into the clustering analysis. A2DKM is qualitatively and quantitatively compared with the conventional clustering algorithms, namely, the K-Means (KM), Fuzzy C-Means (FCM), Moving K-Means (MKM), and Adaptive Fuzzy K-Means (AFKM) algorithms. The A2DKM outperforms these algorithms by producing more homogeneous segmentation results.