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A non-revisiting quantum-behaved particle swarm optimization based multilevel thresholding for image segmentation

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Abstract

Multilevel thresholding for image segmentation is one of the crucial techniques in image processing. Even though numerous methods have been proposed in literature, it is still a challenge for the existing methods to produce steady satisfactory thresholds at manageable computational cost in segmenting images with various unknown properties. In this paper, a non-revisiting quantum-behaved particle swarm optimization (NrQPSO) algorithm is proposed to find the optimal multilevel thresholds for gray-level images. The proposed NrQPSO uses the non-revisiting scheme to avoid the re-evaluation of the evaluated solution candidates. To reduce the unnecessary computation cost, the NrQPSO provides an automatic stopping mechanism which is capable of gauging the progress of exploration and stops the algorithm rationally in a natural manner. For further improving the computation efficiency, the NrQPSO employs a meticulous solution search method to overcome the drawback of the existing QPSO algorithms using the original search methods. Performance of the NrQPSO is tested on the Berkeley segmentation data set. The experimental results have demonstrated that the NrQPSO can outperform the other state-of-the-art population-based thresholding methods in terms of efficiency, effectiveness and robustness; thus, the NrQPSO can be applied in real-time massive image processing.

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Acknowledgements

This study was funded by the Education Department of Guangdong Province of China (Research Grant No. 2017GKTSCX047), the Education Department of Guangzhou City of China (Research Grant No. 201831785), the Technology Department of Guangdong Province of China (Research Grant No. 706049150203) and the Guangzhou Panyu Polytechnic (Research Grant No. 2011Y05PY).

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  1. School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou, 511483, China

    Zhenlun Yang

  2. Department of Electronic Engineering, City University of Hong Kong, Tat Chee Ave, Hong Kong, China

    Angus Wu

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  1. Zhenlun Yang
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Yang, Z., Wu, A. A non-revisiting quantum-behaved particle swarm optimization based multilevel thresholding for image segmentation. Neural Comput & Applic 32, 12011–12031 (2020). https://doi.org/10.1007/s00521-019-04210-z

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