SAR image segmentation based on quantum-inspired multiobjective evolutionary clustering algorithm

The segmentation task in the feature space of an image can be formulated as an optimization problem. Recent researches have demonstrated that the clustering techniques, using only one objective may not obtain suitable solution because the single objective function just can provide satisfactory resul...

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Bibliographic Details
Published in:Information processing letters Vol. 114; no. 6; pp. 287 - 293
Main Authors: Li, Yangyang, Feng, Shixia, Zhang, Xiangrong, Jiao, Licheng
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.06.2014
Elsevier Sequoia S.A
Subjects:
Algorithms
Clustering
Computer science
Evolutionary
Evolutionary algorithms
Heuristic
Image processing systems
Image segmentation
Mathematical models
Mathematical problems
Multiobjective clustering
Optimization algorithms
Quantum computing
SAR image
Simulation
Studies
Synthetic aperture radar
Image segmentation
Multiobjective clustering
Quantum computing
Algorithms
SAR image
ISSN:0020-0190, 1872-6119
Online Access:Get full text
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Summary:The segmentation task in the feature space of an image can be formulated as an optimization problem. Recent researches have demonstrated that the clustering techniques, using only one objective may not obtain suitable solution because the single objective function just can provide satisfactory result to one kind of corresponding data set. In this letter, a novel multiobjective clustering approach, named a quantum-inspired multiobjective evolutionary clustering algorithm (QMEC), is proposed to deal with the problem of image segmentation, where two objectives are simultaneously optimized. Based on the concepts and principles of quantum computing, the multi-state quantum bits are used to represent individuals and quantum rotation gate strategy is used to update the probabilistic individuals. The proposed algorithm can take advantage of the multiobjective optimization mechanism and the superposition of quantum states, and therefore it has a good population diversity and search capabilities. Due to a set of nondominated solutions in multiobjective clustering problems, a simple heuristic method is adopted to select a preferred solution from the final Pareto front and the results show that a good image segmentation result is selected. Experiments on one simulated synthetic aperture radar (SAR) image and two real SAR images have shown the superiority of the QMEC over three other known algorithms. •The image segmentation based on the clustering can be formulated as an optimization problem.•We design a novel multiobjective optimization to solve the image segmentation.•The multi-state quantum bits are used to represent individuals.•A simple heuristic method is adopted to select a preferred solution from the final Pareto front.•Our proposed method is tested on one simulated SAR image and two real SAR images.
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ISSN:0020-0190
1872-6119
DOI:10.1016/j.ipl.2013.12.010