Journal Article10.1016/J.ESWA.2020.114159
A novel Black Widow Optimization algorithm for multilevel thresholding image segmentation
206
TL;DR: The novel meta-heuristic algorithm called Black Widow Optimization (BWO) is introduced to find the best threshold configuration using Otsu or Kapur as objective function and is found to be most promising for multi-level image segmentation problem over other segmentation approaches that are currently used in the literature.
read more
Abstract: Segmentation is a crucial step in image processing applications. This process separates pixels of the image into multiple classes that permits the analysis of the objects contained in the scene. Multilevel thresholding is a method that easily performs this task, the problem is to find the best set of thresholds that properly segment each image. Techniques as Otsu’s between class variance or Kapur’s entropy helps to find the best thresholds but they are computationally expensive for more than two thresholds. To overcome such problem this paper introduces the use of the novel meta-heuristic algorithm called Black Widow Optimization (BWO) to find the best threshold configuration using Otsu or Kapur as objective function. To evaluate the performance and effectiveness of the BWO-based method, it has been considered the use of a variety of benchmark images, and compared against six well-known meta-heuristic algorithms including; the Gray Wolf Optimization (GWO), Moth Flame Optimization (MFO), Whale Optimization Algorithm (WOA), Sine–Cosine Algorithm (SCA), Slap Swarm Algorithm (SSA), and Equilibrium Optimization (EO). The experimental results have revealed that the proposed BWO-based method outperform the competitor algorithms in terms of the fitness values as well as the others performance measures such as PSNR, SSIM and FSIM. The statistical analysis manifests that the BWO-based method achieves efficient and reliable results in comparison with the other methods. Therefore, BWO-based method was found to be most promising for multi-level image segmentation problem over other segmentation approaches that are currently used in the literature.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems
TL;DR: In this paper , a new metaheuristic optimization algorithm called Honey Badger Algorithm (HBA) is proposed, which is inspired from the intelligent foraging behavior of honey badger, to mathematically develop an efficient search strategy for solving optimization problems.
776
Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems
Fatma A. Hashim,Essam H. Houssein,Kashif Hussain,Mai S. Mabrouk,Walid Al-Atabany,Walid Al-Atabany +5 more
TL;DR: The experimental results, along with statistical analysis, reveal the effectiveness of HBA for solving optimization problems with complex search-space, as well as, its superiority in terms of convergence speed and exploration–exploitation balance, as compared to other methods used in this study.
654
Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks
TL;DR: An automated model for detecting and classifying plant leaf diseases using an optimal mobile network-based convolutional neural network (OMNCNN) and extreme learning machine (ELM) based classifier is utilized to allocate proper class labels to the applied plant leaf images.
167
An enhanced black widow optimization algorithm for feature selection
TL;DR: In this paper , an enhanced version of the Black Widow Optimization Algorithm called SDABWO is proposed to solve the feature selection problem, which has faster convergence speed and higher accuracy.
146
A Novel Evolutionary Arithmetic Optimization Algorithm for Multilevel Thresholding Segmentation of COVID-19 CT Images
Laith Abualigah,Laith Abualigah,Ali Diabat,Putra Sumari,Amir H. Gandomi +4 more
- 02 Jul 2021
TL;DR: The proposed DAOA process is better and produces higher-quality solutions than other comparative approaches, and it achieved better-segmented images, PSNR, and SSIM values.
142
References
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
A threshold selection method from gray level histograms
TL;DR: A nonparametric and unsupervised method ofautomatic threshold selection for picture segmentation is presented, whereby an optimal threshold is selected by the discriminant criterion so as to maximize the separability of the resultant classes in gray levels.
44K
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
•Book
Genetic Algorithms
David E. Goldberg,William Shakespeare +1 more
- 01 Jan 2002
TL;DR: The present work expresses the problem as a multi-objective optimization problem and a methodology has been proposed based on multi-objective genetic algo-rithm (MOGA) that exploits the effectiveness of MOGA for searching global optimal solutions in selecting an appropriate image enhancement operator.
17.1K
A new optimizer using particle swarm theory
Russell C. Eberhart,James Kennedy +1 more
- 04 Oct 1995
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
16.4K