FCM-based quantum artificial bee colony algorithm for image segmentation

Feng, Yufang; Yin, Hong; Lu, Houqing; Cao, Lin; Bai, Jingbo

doi:10.1145/3240876.3240907

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…The authors in [97] applied the FCM combined with four-chain quantum bee colony optimization (QABC). This later contributed in the initialization of the clustering centers and reduced the noisy data.…”

Section: Image Segmentation Based On Artificial Bee Colony (Abc)mentioning

confidence: 99%

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

2021

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Image processing is one example of digital media. It consists of a set of operations to handle an image. Image segmentation is among its main important operations. It involves dividing the image into several parts or regions to extract vital information or identify relevant objects. Many techniques of artificial intelligence, including bio-inspired algorithms, have been used in this regard. This article collected the state-of-the-art studies presenting image-segmentation techniques combined with four bio-inspired algorithms including particle swarm optimization (PSO), genetic algorithms (GA), ant colony optimization (ACO), and artificial bee colonies (ABC). This research work aimed at showing the importance of image segmentation and its combination with these algorithms. This article provides insights on how these algorithms are adapted to image-segmentation combinatorial problems, which assist researchers to start the first hands-on application. It also discusses their setting parameters and the highly used algorithms such as PSO, GA, ACO, and ABC. The article presents new research directions in image segmentation based on bio-inspired algorithms.

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Section: Image Segmentation Based On Artificial Bee Colony (Abc)mentioning

confidence: 99%

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

2021

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“…An image segmentation approach takes full advantage of the global optimization search capability of the ABC algorithm and the parallelism of the extended encoding strategy to speed up the convergence speed and improve the exactness of the initial clustering centers of FCM [17] . The salp swarm algorithm to improve multi-threshold GLCM picture segmentation [18] .…”

Section: Introductionmentioning

confidence: 99%

An enhanced artificial bee colony algorithm using radial bases function neural networks for color image segmentation

Shunmuganathan¹,

Sheshathri²,

Sankarasubramanian³

2022

ijhs

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The aim of segmentation process is to partition the image into homogeneous regions or objects. The proposed segmentation algorithms using adaptive feature weighting to distinguish the significance of various features. To extract effective feature extraction could be used for establishing regions in region based image segmentation. The main goal of this exploration work is to segment objects that separating the foreground and background of the color images. The segmentation for color images is focused on the Enhanced Artificial Bee Colony segmentation algorithm that utilizes the Radial Basis Function (EABCRBF) neural network algorithm. This work arranges RBF neural networks to predict the end limits of the segmentation, which are formed from the artificial bee colony province made in the image histogram topography. The radial basis function initial parameters, for instance, centers and widths are automatically set upon the histogram peaks and minima. The proposed EABCRBF strategy is compared with two existing techniques of Artificial Bee Colony (ABC) and Fuzzy C-Means (FCM). The EABCRBF method gives great outcomes for color image segmentation as it extracts additional information from color images. The experimentation results are shown through Mat Lab R2013a.

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