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image thresholding segmentation of generalized fuzzy entropy based on double adaptive ant colony algorithm

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第2期35卷 1979-1990页

作者： Jiang, Shengtao Mu, Xuewen Cheng, Huan Song, Qiyue Xidian Univ Sch Math & Stat 266 Xinglong Sect Xifeng Rd Xian 710126 Shaanxi Peoples R China

A generalized fuzzy entropy based on double adaptive ant colony algorithm for image thresholding segmentation is proposed. The new algorithm first attempts to propose the adaptive pheromone concentration at the initial time and the adaptive global updating rules, which uses the double adaptive mechanism to automatically select the generalized fuzzy entropy parameters. The threshold of the image is obtained by introducing the parameters into the complement of the generalized fuzzy entropy, and then the optimal segmentation of the image is obtained. Compared with the existing image thresholding segmentation algorithms, in most cases, simulating results indicate that the new algorithm has less background information and clearer target information. In addition, it is superior to the existing algorithms in performance and greatly improves the stability and convergence speed.

关键词： Generalized fuzzy entropy image thresholding segmentation double adaptive ant colony algorithm

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Threshold segmentation Based on Fuzzy Kaniadakis Entropy for Criminal Investigation images 21

Threshold Segmentation Based on Fuzzy Kaniadakis Entropy for...

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Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition

作者： Lan Rong Gao Xiaoge School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450384087

Criminal investigation image segmentation is considered one of the most important tasks in the field of criminal investigation image processing. However, the segmentation for criminal investigation images is a challenging task due to natural or human factors. In this paper, an image threshold segmentation algorithm based on fuzzy Kaniadakis entropy was proposed for criminal investigation image segmentation. Firstly, the weighted least squares filter was used for image pre-processing. Then, by using membership functions, image fuzzy sets were constructed based on restricted dissimilarity function. Finally, the maximum fuzzy Kaniadakis entropy was corresponded to the optimal segmentation threshold. The experimental results demonstrate that compared with several existing entropy-based thresholding algorithms, the proposed algorithm is effective in terms of visual effects and segmentation quality measures.

关键词： Criminal investigation image Fuzzy Kaniadakis entropy image thresholding segmentation

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