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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2022
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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 |
_version_ |
1809678026524327936 |