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Threshold image segmentation based on improved sparrow search algorithm
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DOI：10.1007/s11042-022-13073-x.png)
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Abstract
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Threshold segmentation based on swarm intelligence optimization algorithm is a research hotspot in image processing, because of its good segmentation effect and easy implementation. This paper proposes an image threshold segmentation method based on an improved sparrow search algorithm and 2-D maximum entropy method. In the proposed algorithm, the nonlinear inertia weight is introduced into the entrants' update formula to improve the local exploration ability of the algorithm, and Levy flight is introduced into the vigilant sparrows' update formula to prevent the algorithm from falling into the local optimal solution in the later stage of iteration. In addition, improved sparrow search algorithm is tested on fifteen benchmark functions. The results represent the merit of the proposed algorithm with respect to other algorithms. Finally, the proposed algorithm is applied to entropy based image segmentation. Experiment results on classical images and medical images show that the proposed method improves the segmentation effect in terms of peak signal-to-noise ratio and feature similarity.
Keywords:
Swarm optimization algorithm
Threshold image segmentation
2-D histogram
Maximum entropy
Nonlinear weight
Levy flight
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