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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15664

Title: A new particle swarm optimization algorithm for dynamic image clustering
Authors: Salima Ouadfel
Mohamed Batouche
Abdelmalik Taleb-Ahmed
Issue Date: 2010
Abstract: swarm optimization. ACPSO 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 ACPSO outperforms other optimization algorithms.
URI: http://hdl.handle.net/123456789/15664
Appears in Collections:College of Computer and Information Sciences

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