An unsupervised segmentation method based on dynamic threshold neural P systems for color images

Dynamic threshold neural P (DTNP) systems are a new variant of spiking neural P (SNP) systems, abstracted by the spiking and dynamic threshold mechanisms of biological neurons. Motivated by the two mechanisms, this paper develops a novel segmentation method for color images. For this purpose, a 2-dimentional DTNP system with local topology is designed. The spiking mechanism of neurons is used to achieve regional growth, and dynamic threshold is considered to realize the choice of seeds. Moreover, local weights that combine color information can control the regional growth. The proposed segmentation method is evaluated on two benchmark Berkeley segmentation data sets (BSD300 and BSD500) and is compared with 17 state-of-the-art segmentation methods. Experimental results demonstrate the availability and effectiveness of the proposed segmentation method for color images. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a color image segmentation method based on dynamic threshold neural networks, which simulate the spiking and dynamic threshold mechanisms of biological neurons. The method achieves regional growth and seed selection through the spiking mechanism and dynamic threshold, and controls the growth with local weights. Experimental results demonstrate the effectiveness of the proposed method in color image segmentation.