A Novel Practical Decisive Row-Class Entropy-Based Technique for Multilevel Threshold Selection Using Opposition Flow Directional Algorithm

Abstract

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.

Comprehensive analysis of optimal power flow using recent metaheuristic algorithms

“…Liu and Lampinen created the Flow Direction Algorithm (FDA) in 2003 as an optimization technique 37 . It is a metaheuristic algorithm that takes its cues from how water flows through a landscape, always heading towards the lowest point.…”

“…In this paper, Fast Cuckoo Search (FCS) 32 , Salp Swarm Algorithm (SSA) 33 , Dynamic control Cuckoo search (DCCS) 34 , Gradient-Based Optimizer (GBO) 35 , Northern Goshawk Optimization (NGO) 36 , and Opposition Flow Direction Algorithm (OFDA) 37 are utilized for tackling the OPF issue in the standard IEEE 30 Bus test system. A metaheuristic algorithm called Fast Cuckoo Search (FCS) was partly developed due to cuckoo bird behaviour.…”

High-Noise Grayscale Image Denoising Using an Improved Median Filter for the Adaptive Selection of a Threshold

“…Correspondingly, two-dimensional maximum Shannon entropy, representative of the randomness of the variable, can be adopted to explore the information of an image from the relationships between pixels. Thus, an optimal threshold can be automatically calculated using a coupled two-dimensional gray histogram with maximum entropy [29]. Two-dimensional maximum Shannon entropy can be applied in image segmentation [30,31], signal processing [32], and other areas that still lack applications in image denoising, especially in high-density-noise grayscale images.…”