A population-based automatic clustering algorithm for image segmentation

2021 ◽  
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Gerald Schaefer ◽  
Mahshid Helali Moghadam ◽  
Mehrdad Saadatmand ◽  
Mahdi Pedram
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Population Based ◽  
Automatic Clustering

Image Segmentation Using Electromagnetic Field Optimization (EFO) in E-Commerce Applications

2019 ◽  
Vol 10 (3) ◽  
pp. 76-91
Author(s):  
Pankaj Upadhyay ◽  
Jitender Kumar Chhabra
Keyword(s):  
Electromagnetic Field ◽  
Image Segmentation ◽  
Golden Ratio ◽  
Optimal Solution ◽  
Image Data ◽  
Population Based ◽  
Vital Role ◽  
Data Set ◽  
Automatic Clustering ◽  
Segmentation Of Images

Image recognition plays a vital role in image-based product searches and false logo identification on e-commerce sites. For the efficient recognition of images, image segmentation is a very important and is an essential phase. This article presents a physics-inspired electromagnetic field optimization (EFO)-based image segmentation method which works using an automatic clustering concept. The proposed approach is a physics-inspired population-based metaheuristic that exploits the behavior of electromagnets and results into a faster convergence and a more accurate segmentation of images. EFO maintains a balance of exploration and exploitation using the nature-inspired golden ratio between attraction and repulsion forces and converges fast towards a globally optimal solution. Fixed length real encoding schemes are used to represent particles in the population. The performance of the proposed method is compared with recent state of the art metaheuristic algorithms for image segmentation. The proposed method is applied to the BSDS 500 image data set. The experimental results indicate better performance in terms of accuracy and convergence speed over the compared algorithms.

scholarly journals A Grey Wolf Optimizer Based Automatic Clustering Algorithm for Satellite Image Segmentation

2017 ◽  
Vol 115 ◽  
pp. 415-422 ◽  
Author(s):  
Shubham Kapoor ◽  
Irshad Zeya ◽  
Chirag Singhal ◽  
Satyasai Jagannath Nanda
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Satellite Image ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Automatic Clustering

Segmentation of SAR Image using Fuzzy C-Means and Filters

2020 ◽  
Vol 8 (1) ◽  
pp. 84-90
Author(s):  
R. Lalchhanhima ◽  
◽  
Debdatta Kandar ◽  
R. Chawngsangpuii ◽  
Vanlalmuansangi Khenglawt ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
State Of The Art ◽  
Speckle Noise ◽  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Spatial Filters ◽  
Fuzzy C Means ◽  
Automatic Clustering ◽  
Intensity Information

Fuzzy C-Means is an unsupervised clustering algorithm for the automatic clustering of data. Synthetic Aperture Radar Image Segmentation has been a challenging task because of the presence of speckle noise. Therefore the segmentation process can not directly rely on the intensity information alone but must consider several derived features in order to get satisfactory segmentation results. In this paper, it is attempted to use the fuzzy nature of classification for the purpose of unsupervised region segmentation in which FCM is employed. Different features are obtained by filtering of the image by using different spatial filters and are selected for segmentation criteria. The segmentation performance is determined by the accuracy compared with a different state of the art techniques proposed recently.

A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation

2017 ◽  
Vol 32 (04) ◽  
pp. 1850012 ◽  
Author(s):  
R. R. Gharieb ◽  
G. Gendy ◽  
H. Selim
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Local Data ◽  
Cluster Membership ◽  
Kl Divergence ◽  
Clustering Approach ◽  
Center Distance

In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.

scholarly journals An improvedK-means clustering algorithm for fish image segmentation

2013 ◽  
Vol 58 (3-4) ◽  
pp. 790-798 ◽  
Author(s):  
Hong Yao ◽  
Qingling Duan ◽  
Daoliang Li ◽  
Jianping Wang
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm

scholarly journals Locating Optimal Coordinates of Police Deployment and Outpost in Santa Maria Bulacan Using Centroid 360: An Enhanced Centroid Initialization for K means Algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 695-698
Keyword(s):  
Response Time ◽  
Clustering Algorithm ◽  
Population Based ◽  
The Philippines ◽  
Optimum Location ◽  
Mobile Unit ◽  
Police Personnel ◽  
Incident Reports ◽  
The Town ◽  
Santa Maria

The number of police in the Philippines is way below than the declared number of police per population based on the Philippines Republic Act No. 6975, Chapter 3, Section 27 which states that one (1) policeman per five hundred (500) persons. Santa Maria Bulacan only have fifty-two (52) police personnel, and one (1) police outpost which is located in the town of Poblacion which is in the far south east of the whole municipality. This produces different speed when responding to incident reports. The police force’s response time vary based from the location of the incident. This research determined the optimum location to where police will be deployed as a mobile unit to be able to respond faster to more areas than staying in the outpost. Another thing that will be determined by this research is the optimum coordinates of a perfect scenario where each town in the municipality of Santa Maria Bulacan would have their own police outpost. The coordinates will be calculated based on the populations’ location by plotting all the houses and structures located in Santa Maria Bulacan based on Google Map Images, and the Optimum locations will be determined in the form of the converged centroids after applying enhanced K means clustering algorithm.

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