.png){kind=icon}
返回
A Novel Practical Decisive Row-Class Entropy-Based Technique for Multilevel Threshold Selection Using Opposition Flow Directional Algorithm
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2022.3215082.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
One of today's inspiring issues is the 2D histogram-based multilevel threshold selection which is used for segmenting images into several regions. The image analysis warrants exploration of multiclass thresholding techniques using various entropy-based objective functions. In this context, the Shannon type of entropic function without inherent decision making capacity has been widely used for threshold selection in the last decade. Furthermore, a 2D histogram was constructed using local average intensity values resulting in loss of some edge information. To address these problems, this study proposes a new methodology using a novel practical decisive row-class entropy (PDRCE) based fitness function for multilevel thresholding. The PDRCE values are computed using the newly constructed 2D histogram-based on normal local variance. Further, an opposition flow directional algorithm (OFDA) is proposed to maximize the fitness function. The performance of the proposed technique is compared with five state-of-the-art 2D histogram-based entropic fitness functions. Moreover, the performance of OFDA is investigated through comparison with other global optimizers namely the genetic algorithm, particle swarm optimization and artificial bee colony. An image segmentation evaluation dataset (BSDS500) is used in this experiment. It is witnessed that the proposal is more efficient than state-of-the-art methods. Our fitness function would be useful for registration, segmentation, fusion, etc.
Keyword:
Entropy
Image segmentation
Histograms
Linear programming
Thresholding (Imaging)
Decision making
Probability distribution
Image processing
Machine learning
Computational intelligence
Image processing
multilevel thresholding
entropy
computational intelligence
machine learning
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
分享收藏Flow Direction Algorithm (FDA): A Novel Optimization Approach for Solving Optimization Problems流向算法 (FDA): 一种解决优化问题的新型优化方法
PREAI
分享收藏Population-based self-adaptive Generalised Masi Entropy for image segmentation: A novel representation基于种群的自适应广义Masi熵图像分割: 一种新的表示方法
PREAI
分享收藏Hybrid Aquila optimizer with arithmetic optimization algorithm for global optimization tasks具有全局优化任务的算术优化算法的混合Aquila优化器.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏
查看更多内容