scholarly journals A Fast K-medoids Clustering Algorithm for Image Segmentation based Object Recognition

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Wang Xiaochun ◽  
Wang Xia Li
Keyword(s):  
Image Segmentation ◽  
Object Recognition ◽  
Clustering Algorithm

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

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.

Multiband Image Segmentation and Object Recognition for Understanding Road Scenes

2011 ◽  
Vol 12 (4) ◽  
pp. 1423-1433 ◽  
Author(s):  
Yousun Kang ◽  
Koichiro Yamaguchi ◽  
Takashi Naito ◽  
Yoshiki Ninomiya
Keyword(s):  
Image Segmentation ◽  
Object Recognition

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
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