Selective Image Segmentation Models Using Three Distance Functions

Image segmentation can be defined as partitioning an image that contains multiple segments of meaningful parts for further processing. Global segmentation is concerned with segmenting the whole object of an observed image. Meanwhile, the selective segmentation model is focused on segmenting a specif...

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Published in:Journal of Information and Communication Technology
Main Author: Abdullah S.A.; Jumaat A.K.
Format: Article
Language:English
Published: Universiti Utara Malaysia Press 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120975730&doi=10.32890%2fjict2022.21.1.5&partnerID=40&md5=45974beb8f8b110eb22ce03d617d01b4
id 2-s2.0-85120975730
spelling 2-s2.0-85120975730
Abdullah S.A.; Jumaat A.K.
Selective Image Segmentation Models Using Three Distance Functions
2022
Journal of Information and Communication Technology
21
1
10.32890/jict2022.21.1.5
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120975730&doi=10.32890%2fjict2022.21.1.5&partnerID=40&md5=45974beb8f8b110eb22ce03d617d01b4
Image segmentation can be defined as partitioning an image that contains multiple segments of meaningful parts for further processing. Global segmentation is concerned with segmenting the whole object of an observed image. Meanwhile, the selective segmentation model is focused on segmenting a specific object required to be extracted. The Convex Distance Selective Segmentation (CDSS) model, which uses the Euclidean distance function as the fitting term, was proposed in 2015. However, the Euclidean distance function takes time to compute. This paper proposed the reformulation of the CDSS minimization problem by changing the fitting term with three popular distance functions, namely Chessboard, City Block, and Quasi-Euclidean. The proposed models were CDSSNEW1, CDSSNEW2, and CDSSNEW3, which applied the Chessboard, City Block, and Quasi-Euclidean distance functions, respectively. In this study, the Euler-Lagrange (EL) equations of the proposed models were derived and solved using the Additive Operator Splitting method. Then, MATLAB coding was developed to implement the proposed models.The accuracy of the segmented image was evaluated using the Jaccard and Dice Similarity Coefficients. The execution time was recorded to measure the efficiency of the models. Numerical results showed that the proposed CDSSNEW1 model based on the Chessboard distance function could segment specific objects successfully for all grayscale images with the fastest execution time as compared to other models © 2022, Journal of Information and Communication Technology. All Rights Reserved.
Universiti Utara Malaysia Press
1675414X
English
Article
All Open Access; Gold Open Access
author Abdullah S.A.; Jumaat A.K.
spellingShingle Abdullah S.A.; Jumaat A.K.
Selective Image Segmentation Models Using Three Distance Functions
author_facet Abdullah S.A.; Jumaat A.K.
author_sort Abdullah S.A.; Jumaat A.K.
title Selective Image Segmentation Models Using Three Distance Functions
title_short Selective Image Segmentation Models Using Three Distance Functions
title_full Selective Image Segmentation Models Using Three Distance Functions
title_fullStr Selective Image Segmentation Models Using Three Distance Functions
title_full_unstemmed Selective Image Segmentation Models Using Three Distance Functions
title_sort Selective Image Segmentation Models Using Three Distance Functions
publishDate 2022
container_title Journal of Information and Communication Technology
container_volume 21
container_issue 1
doi_str_mv 10.32890/jict2022.21.1.5
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120975730&doi=10.32890%2fjict2022.21.1.5&partnerID=40&md5=45974beb8f8b110eb22ce03d617d01b4
description Image segmentation can be defined as partitioning an image that contains multiple segments of meaningful parts for further processing. Global segmentation is concerned with segmenting the whole object of an observed image. Meanwhile, the selective segmentation model is focused on segmenting a specific object required to be extracted. The Convex Distance Selective Segmentation (CDSS) model, which uses the Euclidean distance function as the fitting term, was proposed in 2015. However, the Euclidean distance function takes time to compute. This paper proposed the reformulation of the CDSS minimization problem by changing the fitting term with three popular distance functions, namely Chessboard, City Block, and Quasi-Euclidean. The proposed models were CDSSNEW1, CDSSNEW2, and CDSSNEW3, which applied the Chessboard, City Block, and Quasi-Euclidean distance functions, respectively. In this study, the Euler-Lagrange (EL) equations of the proposed models were derived and solved using the Additive Operator Splitting method. Then, MATLAB coding was developed to implement the proposed models.The accuracy of the segmented image was evaluated using the Jaccard and Dice Similarity Coefficients. The execution time was recorded to measure the efficiency of the models. Numerical results showed that the proposed CDSSNEW1 model based on the Chessboard distance function could segment specific objects successfully for all grayscale images with the fastest execution time as compared to other models © 2022, Journal of Information and Communication Technology. All Rights Reserved.
publisher Universiti Utara Malaysia Press
issn 1675414X
language English
format Article
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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