Add to ORKGIn this paper, we present a new automatic image clustering algorithm based on a modified version of particle swarm optimization algorithm. ACMPSO clustering algorithm can partition image into compact and well separated clusters without any knowledge on the real number of clusters. It uses a swarm of particles with variable number of length, which evolve dynamically using mutation operators. Experimental results on real images demonstrate that the proposed algorithm is able to extract the correct number of clusters with denser and more compactness clusters. The results demonstrate that ACMPSOoutperforms other optimization algorithms
[[abstract]]Clustering analysis is applied generally to pattern recognition, color quantization and ...
Unsupervised fuzzy clustering algorithms are one of many approaches used in image segmentation. The ...
<div><p>This paper puts forward a new automatic clustering algorithm based on Multi-Objective Partic...
swarm optimization. ACPSO can partition image into compact and well separated clusters without any k...
A new dynamic clustering approach (DCPSO), based on Particle Swarm Optimization, is proposed. This a...
Clustering plays important role in many areas such as medical applications, pattern recognition, ima...
Clustering plays important role in many areas such as medical applications, pattern recognition, ima...
International audienceIn this paper, we propose an improvement method for image segmentation problem...
Color image segmentation is a fundamental challenge in the field of image analysis and pattern recog...
This paper presents a new approach of dynamic clustering based on improved Particle Swarm Optimizati...
Ant Colony Optimization (ACO) is a newly proposed intelligent algorithm for solving discrete optimiz...
Unsupervised fuzzy clustering algorithms are one of many approaches used in image segmentation. The ...
Unsupervised fuzzy clustering algorithms are one of many approaches used in image segmentation. The ...
The clustering problem has been studied by many researchers using various approaches, including tabu...
In order to overcome the shortcomings of traditional clustering algorithms such as local optima and ...
[[abstract]]Clustering analysis is applied generally to pattern recognition, color quantization and ...
Unsupervised fuzzy clustering algorithms are one of many approaches used in image segmentation. The ...
<div><p>This paper puts forward a new automatic clustering algorithm based on Multi-Objective Partic...
swarm optimization. ACPSO can partition image into compact and well separated clusters without any k...
A new dynamic clustering approach (DCPSO), based on Particle Swarm Optimization, is proposed. This a...
Clustering plays important role in many areas such as medical applications, pattern recognition, ima...
Clustering plays important role in many areas such as medical applications, pattern recognition, ima...
International audienceIn this paper, we propose an improvement method for image segmentation problem...
Color image segmentation is a fundamental challenge in the field of image analysis and pattern recog...
This paper presents a new approach of dynamic clustering based on improved Particle Swarm Optimizati...
Ant Colony Optimization (ACO) is a newly proposed intelligent algorithm for solving discrete optimiz...
Unsupervised fuzzy clustering algorithms are one of many approaches used in image segmentation. The ...
Unsupervised fuzzy clustering algorithms are one of many approaches used in image segmentation. The ...
The clustering problem has been studied by many researchers using various approaches, including tabu...
In order to overcome the shortcomings of traditional clustering algorithms such as local optima and ...
[[abstract]]Clustering analysis is applied generally to pattern recognition, color quantization and ...
Unsupervised fuzzy clustering algorithms are one of many approaches used in image segmentation. The ...
<div><p>This paper puts forward a new automatic clustering algorithm based on Multi-Objective Partic...