Bloch quantum artificial bee colony algorithm and its application in image threshold segmentation

Huo, Fengcai; Liu, Yang; Wang, Di; Sun, Bo

doi:10.1007/s11760-017-1123-6

Cited by 19 publications

(8 citation statements)

References 17 publications

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“…Hence, the higher the fitness is, the greater the swarm finds the threshold to segment the target from the background. Common swarm intelligence algorithms include the whale optimization algorithm [1,29], Harris hawks optimization [2], artificial neural networks [3,11,30], deep learning [4,12,21,38], gray wolf optimization [5,39], particle swarm optimization [7,23,40], differential evolution algorithm [9], cuckoo search algorithm [10], ant colony optimization [13,33], genetic algorithm [14,40], artificial bee colony algorithm [15,25], sparrow search algorithm [16], moth swarm algorithm (MSA) [24], emperor penguin optimization (EPO) [26], marine predators algorithm (MPA) [27], salp swarm algorithm (SSA) [27], firefly algorithm (FA) [28], Aptenodytes Forsteri optimization algorithm (AFOA) [32], artificial fish swarm algorithm (AFSA) [36], artificial plant community (APC) [41,42], krill swarm (KS) [43], immune system (IS) [44], naked mole-rat algorithm (NMRA) [45], attention mechanism [46], and so on.…”

Section: Related Workmentioning

confidence: 99%

“…Among them, swarm intelligence is a hotspot that simulates the behaviors of a swarm of animals in foraging and living. For example, the whale optimization algorithm (WOA) [1], Harris hawks optimization (HHO) [2], artificial neural networks (ANNs) [3,11], deep learning (DL) [4,12], gray wolf optimization (GWO) [5], particle swarm optimization (PSO) [7], differential evolution algorithm (DEA) [9], cuckoo search algorithm (CSA) [10], ant colony optimization (ACO) [13], genetic algorithm (GA) [14], artificial bee colony algorithm (ABC) [15], sparrow search algorithm (SSA) [16], and so on. Recent works verified that swarm intelligence algorithms can obtain good performance and are promising to solve image segmentation problems.…”

Section: Introductionmentioning

confidence: 99%

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Using an Artificial Physarum polycephalum Colony for Threshold Image Segmentation

Cai,

Li,

Zhang

et al. 2023

Applied Sciences

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Traditional artificial intelligence algorithms are prone to falling into local optima when solving threshold segmentation problems. Here, a novel artificial Physarum polycephalum colony algorithm is proposed to help us solve the difficult problem. First, the algorithm methodology of an artificial Physarum polycephalum colony algorithm is described to search for the optimal solutions by expansion and contraction of a lot of artificial hyphae. Different artificial Physarum polycephalum can learn from each other and produce more hyphae in expansion. In contraction, the artificial Physarum polycephalum colony can select the best hyphae with high fitness through a quick sort algorithm, but the other hyphae with low fitness will be absorbed and disappear. Second, a fitness function is modeled based on Kapur’s entropy for the proposed artificial Physarum polycephalum colony algorithm to search for optimal threshold segmentation solutions. Third, a series of benchmark experiments are implemented to test the proposed artificial Physarum polycephalum colony algorithm, and some state-of-the-art approaches are employed for comparison. The experimental results verified that the proposed algorithm can obtain better accuracy and convergence speed, and is not easier to fall into the local optimal solution too early.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using an Artificial Physarum polycephalum Colony for Threshold Image Segmentation

Cai,

Li,

Zhang

et al. 2023

Applied Sciences

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“…The authors of [95] proposed an improved ABC method that utilized quantum and space coordinates to find the optimal segmentation threshold of the images. The proposed method (BQABC) used Bloch spherical coordinates to encode the cubits in order to initialize the food sources.…”

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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“…The third approach, object-oriented image segmentation, treats objects as independent entities using regional segmentation.This method classifies objects based on the homogeneous nature of pixels.Common algorithms include edge detection [13], thresholding [14], and region-based methods [15].Literature [16][17][18] moves from pixel to region level, using image objects to build multiscale region expressions and describe texture, reducing pixel segmentation artifacts.However, these methods need scale adjustments for optimal results and struggle to express geographic spatial relationships.When image features are small and dispersed, results may be less accurate.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Segmentation for Geospatial Entities

Geng,

Lv,

Wang

et al. 2024

Preprint

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Remote sensing image segmentation supports image interpretation.However, current methods yield results limited to segmented maps, showing only objects' boundary contours and positions.In remote sensing images, there are deeper connections between objects, such as spatial distance and topological relationships. Extracting the relationship features between objects on the basis of segmentation can significantly enhance image information. Since geographical entities contain rich attribute and spatiotemporal relationship features, which can compensate for the shortcomings of current remote sensing image segmentation, this paper proposes a remote sensing image segmentation algorithm oriented towards geographical entities. Through this method, rich and dynamic segmentation results are obtained, including three main aspects: first, segmenting the boundary contours of geographical entities using an attribute attention mechanism, extracting semantic, geometric, and visual information of entities; second, establishing a temporal attribute matrix to describe changes in the image over time; third, extracting orientation distance, topological, and interaction relationships between entities based on a semantic network model. Finally, the results obtained by this method include an additional segmentation information table based on the segmented map, which can comprehensively demonstrate the interaction relationships between entities in the image, providing new insights for remote sensing image interpretation.

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Bloch quantum artificial bee colony algorithm and its application in image threshold segmentation

Cited by 19 publications

References 17 publications

Using an Artificial Physarum polycephalum Colony for Threshold Image Segmentation

Using an Artificial Physarum polycephalum Colony for Threshold Image Segmentation

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

Remote Sensing Image Segmentation for Geospatial Entities

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