An Image Thresholding Method Based on Differential Evolution Algorithm and Genetic Algorithm

Ye, Zhiwei; Zhang, Aixin; Cao, Ye; Ma, Lie; Jin, Can; Hu, Xiang; Hu, Jiwei

doi:10.1109/idaacs.2019.8924335

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Genetic algorithm is an randomized and optimized method guided by the principles of evolution and natural genetics. It is widely used in various tasks [43], [44], [45], [46] because of its high efficiency, adaptability and robustness. And it can produce near-optimal solutions and can handle large, highly complex and multi-modal spaces.…”

Section: Genetic-based Approachmentioning

confidence: 99%

Multi-Knowledge Fusion Network for Generalized Zero-Shot Learning

Xiang

Xie

Zeng

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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Suffering from the semantic insufficiency and domain-shift problems, most of existing state-of-the-art methods fail to achieve satisfactory results for Zero-Shot Learning (ZSL). In order to alleviate these problems, we propose a novel generative ZSL method to learn more generalized features from multi-knowledge in semantic-to-visual embedding. In our approach, the proposed Multi-Knowledge Fusion Network (MKFNet) alleviates the semantic insufficiency problem by fusing the domain information of different knowledge, which enables more relevant semantic features to be trained for semantic-to-visual feature embedding. The proposed knowledge regularization L KR greatly improves the intersection between the synthesized visual features generated by MKFNet and the unseen visual features, which can alleviate the domain-shift problem. Empirically, we show that our approach consistently outperforms these state-of-the-art methods on a large number of available benchmarks on the generalized ZSL (GZSL).

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Section: Genetic-based Approachmentioning

confidence: 99%

Multi-Knowledge Fusion Network for Generalized Zero-Shot Learning

Xiang

Xie

Zeng

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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An Adaptive Bi-Mutation-Based Differential Evolution Algorithm for Multi-Threshold Image Segmentation

Sun

Yang

2022

Applied Sciences

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Aiming at solving the problems of large calculation, time-consuming, and low segmentation accuracy of multi-threshold image segmentation, an adaptive threshold value based differential evolution algorithm is proposed in this paper. Firstly, an opposite learning strategy is introduced into the initial population to improve the quality of the initial population; secondly, a threshold-value-based mutation strategy is proposed to balance the exploration and development capabilities of the algorithm, and the number of successfully evolved individuals is considered as a threshold value to adaptively adjust the evolution of superior and inferior individuals. Experiments demonstrate that the proposed algorithm has better performance in enhancing accuracy and speeding up the convergence.

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