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Segmentation of Natural Images with K-Means and Hierarchical Algorithm based on Mixture of Pearson Distributions

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Title Segmentation of Natural Images with K-Means and Hierarchical Algorithm based on Mixture of Pearson Distributions
 
Creator Sekhar, P Chandra
Rao, N Thirupathi
Bhattacharyya, Debnath
Kim, Tai-hoon
 
Subject Berkeley image database
EM-algorithm
Image quality metrics
Non-symmetric model
Type III Pearsonian distribution
 
Description 707-715
In this paper, an attempt has been made to analyze the performance of the image segmented algorithms with the addition
of the Pearsonian Type III mixture model. By using the Type III Pearsonian system of distributions the image segmentation
process was carried out in the current article which is a novel technique. With the help of K-component combination of
Pearsonian Type III distribution, it is considered that the whole input images are characterized. The performance parameters
PRI (Probabilistic Rand Index), GCE (Global Consistency Error) and VOI (Volume of Interest) for the currently considered
model are estimated with the help of EM (Expectation Maximization) algorithm. For analyzing the proposed model’s
performance, four random images are selected as input for the current model from Berkeley image database. The
performance metric parameters PRI, GCE and VOI values given the results as the currently proposed method is providing
more précise results for the input images where the regions of the input images selected are with tiles having long upper
model and the left skewed images. By the help of image quality measures, the proposed method is performing well for the
purpose of retrieving the images with respect to the picture segmenting process which is based on GMM (Gaussian Mixture
Model). The current model performance was compared with the other existing models like the k-means hierarchical
clustering model and the 3-paprameter regression models.
 
Date 2021-09-01T10:47:42Z
2021-09-01T10:47:42Z
2021-08
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/57981
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.80(08) [August 2021]