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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15690

Title: A Quantum-Inspired Evolutionary Algorithm for Multiobjective Image Segmentation
Authors: Hichem Talbi
Mohamed Batouche
Amer Draa
Keywords: Image segmentation, multiobjective optimization,quantum computing, evolutionary algorithms
Issue Date: 2007
Abstract: In this paper we present a new approach to deal with image segmentation. The fact that a single segmentation result do not generally allow a higher level process to take into account all the elements included in the image has motivated the consideration of image segmentation as a multiobjective optimization problem. The proposed algorithm adopts a split/merge strategy that uses the result of the k-means algorithm as input for a quantum evolutionary algorithm to establish a set of non-dominated solutions. The evaluation is made simultaneously according to two distinct features: intra-region homogeneity and inter-region heterogeneity. The experimentation of the new approach on natural images has proved its efficiency and usefulness
URI: http://hdl.handle.net/123456789/15690
Appears in Collections:College of Computer and Information Sciences

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