An Efficient Image Segmentation Algorithm Using Neutrosophic Graph Cut
Segmentation is considered as an important step in image processing and computer vision applications, which divides an input image into various non-overlapping homogenous regions and helps to interpret the image more conveniently. This paper presents an efficient image segmentation algorithm using n...
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doaj-c2d2e7861b2a442c8feb4f5deed94a662020-11-25T00:29:48ZengMDPI AGSymmetry2073-89942017-09-019918510.3390/sym9090185sym9090185An Efficient Image Segmentation Algorithm Using Neutrosophic Graph CutYanhui Guo0Yaman Akbulut1Abdulkadir Şengür2Rong Xia3Florentin Smarandache4Department of Computer Science, University of Illinois at Springfield, Springfield, IL 62703, USADepartment of Electrical and Electronics Engineering, Firat University, 23119 Elazig, TurkeyDepartment of Electrical and Electronics Engineering, Firat University, 23119 Elazig, TurkeyOracle Corporation, Westminster, CO 80021, USAMathematics & Science Department, University of New Mexico, Gallup, NM 87301, USASegmentation is considered as an important step in image processing and computer vision applications, which divides an input image into various non-overlapping homogenous regions and helps to interpret the image more conveniently. This paper presents an efficient image segmentation algorithm using neutrosophic graph cut (NGC). An image is presented in neutrosophic set, and an indeterminacy filter is constructed using the indeterminacy value of the input image, which is defined by combining the spatial information and intensity information. The indeterminacy filter reduces the indeterminacy of the spatial and intensity information. A graph is defined on the image and the weight for each pixel is represented using the value after indeterminacy filtering. The segmentation results are obtained using a maximum-flow algorithm on the graph. Numerous experiments have been taken to test its performance, and it is compared with a neutrosophic similarity clustering (NSC) segmentation algorithm and a graph-cut-based algorithm. The results indicate that the proposed NGC approach obtains better performances, both quantitatively and qualitatively.https://www.mdpi.com/2073-8994/9/9/185image segmentationneutrosophic setgraph cutindeterminate filtering |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yanhui Guo Yaman Akbulut Abdulkadir Şengür Rong Xia Florentin Smarandache |
spellingShingle |
Yanhui Guo Yaman Akbulut Abdulkadir Şengür Rong Xia Florentin Smarandache An Efficient Image Segmentation Algorithm Using Neutrosophic Graph Cut Symmetry image segmentation neutrosophic set graph cut indeterminate filtering |
author_facet |
Yanhui Guo Yaman Akbulut Abdulkadir Şengür Rong Xia Florentin Smarandache |
author_sort |
Yanhui Guo |
title |
An Efficient Image Segmentation Algorithm Using Neutrosophic Graph Cut |
title_short |
An Efficient Image Segmentation Algorithm Using Neutrosophic Graph Cut |
title_full |
An Efficient Image Segmentation Algorithm Using Neutrosophic Graph Cut |
title_fullStr |
An Efficient Image Segmentation Algorithm Using Neutrosophic Graph Cut |
title_full_unstemmed |
An Efficient Image Segmentation Algorithm Using Neutrosophic Graph Cut |
title_sort |
efficient image segmentation algorithm using neutrosophic graph cut |
publisher |
MDPI AG |
series |
Symmetry |
issn |
2073-8994 |
publishDate |
2017-09-01 |
description |
Segmentation is considered as an important step in image processing and computer vision applications, which divides an input image into various non-overlapping homogenous regions and helps to interpret the image more conveniently. This paper presents an efficient image segmentation algorithm using neutrosophic graph cut (NGC). An image is presented in neutrosophic set, and an indeterminacy filter is constructed using the indeterminacy value of the input image, which is defined by combining the spatial information and intensity information. The indeterminacy filter reduces the indeterminacy of the spatial and intensity information. A graph is defined on the image and the weight for each pixel is represented using the value after indeterminacy filtering. The segmentation results are obtained using a maximum-flow algorithm on the graph. Numerous experiments have been taken to test its performance, and it is compared with a neutrosophic similarity clustering (NSC) segmentation algorithm and a graph-cut-based algorithm. The results indicate that the proposed NGC approach obtains better performances, both quantitatively and qualitatively. |
topic |
image segmentation neutrosophic set graph cut indeterminate filtering |
url |
https://www.mdpi.com/2073-8994/9/9/185 |
work_keys_str_mv |
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