scholarly journals Metaheuristic Algorithms Applied to Color Image Segmentation on HSV Space

2022 ◽  
Vol 8 (1) ◽  
pp. 6
Author(s):  
Donatella Giuliani
Keyword(s):  
Artificial Bee Colony ◽  
Similarity Index ◽  
Gaussian Mixture ◽  
Metaheuristic Algorithms ◽  
Color Image Segmentation ◽  
Inner Product ◽  
Second Phase ◽  
Edge Extraction ◽  
Color Channel ◽  
Bee Colony

In this research, we propose an unsupervised method for segmentation and edge extraction of color images on the HSV space. This approach is composed of two different phases in which are applied two metaheuristic algorithms, respectively the Firefly (FA) and the Artificial Bee Colony (ABC) algorithms. In the first phase, we performed a pixel-based segmentation on each color channel, applying the FA algorithm and the Gaussian Mixture Model. The FA algorithm automatically detects the number of clusters, given by histogram maxima of each single-band image. The detected maxima define the initial means for the parameter estimation of the GMM. Applying the Bayes’ rule, the posterior probabilities of the GMM can be used for assigning pixels to clusters. After processing each color channel, we recombined the segmented components in the final multichannel image. A further reduction in the resultant cluster colors is obtained using the inner product as a similarity index. In the second phase, once we have assigned all pixels to the corresponding classes of the HSV space, we carry out the second step with a region-based segmentation applied to the corresponding grayscale image. For this purpose, the bioinspired Artificial Bee Colony algorithm is performed for edge extraction.

scholarly journals An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

2019 ◽  
Vol 2019 ◽  
pp. 1-25
Author(s):  
Yanzhu Hu ◽  
Jiao Wang ◽  
Xinbo Ai ◽  
Xu Zhuang
Keyword(s):  
Cost Function ◽  
Artificial Bee Colony ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Segmentation Algorithm ◽  
Bee Colony ◽  
Segmentation Of Images ◽  
The Cost

In order to realize the multithreshold segmentation of images, an improved segmentation algorithm based on graph cut theory using artificial bee colony is proposed. A new weight function based on gray level and the location of pixels is constructed in this paper to calculate the probability that each pixel belongs to the same region. On this basis, a new cost function is reconstructed that can use both square and nonsquare images. Then the optimal threshold of the image is obtained through searching for the minimum value of the cost function using artificial bee colony algorithm. In this paper, public dataset for segmentation and widely used images were measured separately. Experimental results show that the algorithm proposed in this paper can achieve larger Information Entropy (IE), higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), smaller Root Mean Squared Error (RMSE), and shorter time than other image segmentation algorithms.

scholarly journals Artificial Bee Colony Optimization Based Despeckling Framework for Ultrasound Images

2020 ◽  
Vol 13 (5) ◽  
pp. 20-32
Author(s):  
Pradeep K. Gupta ◽  
◽  
Shyam Lal ◽  
Farooq Husain
Keyword(s):  
Artificial Bee Colony ◽  
Similarity Index ◽  
Absolute Error ◽  
Thoracic Wall ◽  
Ultrasound Images ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

This paper proposed an artificial bee colony optimization (ABC) algorithm based despeckling framework to overcome the effect of speckle noise present in real ultrasound images. A low pass filter and fast non-local mean filter along with Artificial Bee Colony (ABC) optimization algorithm are used for the quality enhancement of ultrasound images. The output results obtained for the real ultrasound images filtered with the proposed approach and the other most studied approaches discussed in the literature. The outperformance of the proposed method is verified by calculation of peak signal to noise ratio (PSNR), mean square error (MSE), mean absolute error (MAE), and structure similarity index (SSIM) quality measures. The proposed filtering approach is tested on eight real clinical ultrasound images of adrenal gland, appendicitis, bladder, pancreas, parathyroid gland, scrotal gland, thoracic wall, and uterus. The experimental results yield that the quantitative and qualitative results of the proposed framework are better than benchmark despeckling methods compared to real ultrasound images. Further, the proposed framework also preserves the fine details in real ultrasound images.

A Hybrid Nature-Inspired Artificial Bee Colony Algorithm for Uncapacitated Examination Timetabling Problems

2015 ◽  
Vol 24 (1) ◽  
pp. 37-54 ◽  
Author(s):  
Asaju La’aro Bolaji ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Mohammed A. Awadallah
Keyword(s):  
Artificial Bee Colony ◽  
Search Algorithm ◽  
Harmony Search ◽  
Search Space ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Second Phase ◽  
Examination Timetabling ◽  
Bee Colony ◽  
Population Based Algorithm

AbstractThis article presents a Hybrid Artificial Bee Colony (HABC) for uncapacitated examination timetabling. The ABC algorithm is a recent metaheuristic population-based algorithm that belongs to the Swarm Intelligence technique. Examination timetabling is a hard combinatorial optimization problem of assigning examinations to timeslots based on the given hard and soft constraints. The proposed hybridization comes in two phases: the first phase hybridized a simple local search technique as a local refinement process within the employed bee operator of the original ABC, while the second phase involves the replacement of the scout bee operator with the random consideration concept of harmony search algorithm. The former is to empower the exploitation capability of ABC, whereas the latter is used to control the diversity of the solution search space. The HABC is evaluated using a benchmark dataset defined by Carter, including 12 problem instances. The results show that the HABC is better than exiting ABC techniques and competes well with other techniques from the literature.

Segmentation and Edge Extraction of Grayscale Images Using Firefly and Artificial Bee Colony Algorithms

2020 ◽  
pp. 78-99
Author(s):  
Donatella Giuliani
Keyword(s):  
Firefly Algorithm ◽  
Artificial Bee Colony ◽  
Spatial Information ◽  
Search Space ◽  
Gaussian Mixture ◽  
Food Sources ◽  
Bayes Rule ◽  
Prior Probabilities ◽  
Bee Colony ◽  
Homogeneous Regions

This chapter proposes an unsupervised grayscale image segmentation method based on the Firefly and Artificial Bee Colony algorithms. The Firefly Algorithm is applied in a histogram-based research of cluster centroids to determine the number of clusters and the gray levels, successively used in the initialization step for the parameter estimation of a Gaussian Mixture Model. The coefficients of the linear super-position of Gaussians can be thought of as prior probabilities of each component. Applying the Bayes rule, the posterior probabilities of the grayscale intensities are evaluated and their maxima are used to assign each pixel to clusters. Subsequently, region spatial information is extracted to form homogeneous regions through ABC algorithm. Initially, scout bees are moving on the search space describing random paths, with food sources given by the detected homogeneous regions. Then onlooker bees rush to scouts' aid proportionally to unclassified pixels enclosed into the bounded boxes of the discovered regions.

scholarly journals Artificial Bee Colony (ABC) algorithm and its use in digital image processing

2015 ◽  
Vol 18 (55) ◽  
pp. 50
Author(s):  
Erik Cuevas
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Gaussian Mixture ◽  
Biological Behaviour ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Edge Points

Classical methods often face great difficulties in solving image processing problems in images containing noise and distortions. Under such conditions, the use of bio-inspired optimization approaches has been extended. This paper explores the use of the Artificial Bee Colony (ABC) algorithm for digital image processing seen as an optimization problem. ABC is a heuristic algorithm motivated by the biological behaviour of honey-bees which has been successfully employed to solve complex optimization problems. In this paper, image segmentation and circle detection tasks are considered as examples, both issues approached as optimization problems. In segmentation, an image 1-D histogram is approximated through a Gaussian mixture model whose parameters are calculated by the ABC algorithm. On the other hand, the circle detector uses a combination of three edge points as parameters to construct candidate circles. A matching function determines is such candidate circles are actually present in the image. Experimental results show that the generated solutions are able to solve properly the considered problems.

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