Image Segmentation using Improved Imperialist Competitive Algorithm and a Simple Post-processing
Image segmentation is a fundamental step in many of image processing applications. In most cases the image’s pixels are clustered only based on the pixels’ intensity or color information and neither spatial nor neighborhood information of pixels is used in the clustering process. Considering the imp...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Shahrood University of Technology
2019-11-01
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| Series: | Journal of Artificial Intelligence and Data Mining |
| Subjects: | |
| Online Access: | http://jad.shahroodut.ac.ir/article_1465_2ad4291fcc7695bdd431b925bdb3290a.pdf |
| _version_ | 1818822779874574336 |
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| author | V. Naghashi Sh. Lotfi |
| author_facet | V. Naghashi Sh. Lotfi |
| author_sort | V. Naghashi |
| collection | DOAJ |
| description | Image segmentation is a fundamental step in many of image processing applications. In most cases the image’s pixels are clustered only based on the pixels’ intensity or color information and neither spatial nor neighborhood information of pixels is used in the clustering process. Considering the importance of including spatial information of pixels which improves the quality of image segmentation, and using the information of the neighboring pixels, causes enhancing of the accuracy of segmentation. In this paper the idea of combining the K-means algorithm and the Improved Imperialist Competitive algorithm is proposed. Also before applying the hybrid algorithm, a new image is created and then the hybrid algorithm is employed. Finally, a simple post-processing is applied on the clustered image. Comparing the results of the proposed method on different images, with other methods, shows that in most cases, the accuracy of the NLICA algorithm is better than the other methods. |
| first_indexed | 2024-12-18T23:29:31Z |
| format | Article |
| id | doaj.art-463dbc9fd1a04913a3413f96cbfa4b57 |
| institution | Directory Open Access Journal |
| issn | 2322-5211 2322-4444 |
| language | English |
| last_indexed | 2024-12-18T23:29:31Z |
| publishDate | 2019-11-01 |
| publisher | Shahrood University of Technology |
| record_format | Article |
| series | Journal of Artificial Intelligence and Data Mining |
| spelling | doaj.art-463dbc9fd1a04913a3413f96cbfa4b572022-12-21T20:47:43ZengShahrood University of TechnologyJournal of Artificial Intelligence and Data Mining2322-52112322-44442019-11-017450751910.22044/jadm.2019.3935.14641465Image Segmentation using Improved Imperialist Competitive Algorithm and a Simple Post-processingV. Naghashi0Sh. Lotfi1Computer Engineering, University College of Nabi Akram, Rahahan, Tabriz, Iran.Computer Science, University of Tabriz, Tabriz, Iran.Image segmentation is a fundamental step in many of image processing applications. In most cases the image’s pixels are clustered only based on the pixels’ intensity or color information and neither spatial nor neighborhood information of pixels is used in the clustering process. Considering the importance of including spatial information of pixels which improves the quality of image segmentation, and using the information of the neighboring pixels, causes enhancing of the accuracy of segmentation. In this paper the idea of combining the K-means algorithm and the Improved Imperialist Competitive algorithm is proposed. Also before applying the hybrid algorithm, a new image is created and then the hybrid algorithm is employed. Finally, a simple post-processing is applied on the clustered image. Comparing the results of the proposed method on different images, with other methods, shows that in most cases, the accuracy of the NLICA algorithm is better than the other methods.http://jad.shahroodut.ac.ir/article_1465_2ad4291fcc7695bdd431b925bdb3290a.pdfimage segmentationclusteringimproved imperialist competitive algorithmpost-processingberkley images dataset |
| spellingShingle | V. Naghashi Sh. Lotfi Image Segmentation using Improved Imperialist Competitive Algorithm and a Simple Post-processing Journal of Artificial Intelligence and Data Mining image segmentation clustering improved imperialist competitive algorithm post-processing berkley images dataset |
| title | Image Segmentation using Improved Imperialist Competitive Algorithm and a Simple Post-processing |
| title_full | Image Segmentation using Improved Imperialist Competitive Algorithm and a Simple Post-processing |
| title_fullStr | Image Segmentation using Improved Imperialist Competitive Algorithm and a Simple Post-processing |
| title_full_unstemmed | Image Segmentation using Improved Imperialist Competitive Algorithm and a Simple Post-processing |
| title_short | Image Segmentation using Improved Imperialist Competitive Algorithm and a Simple Post-processing |
| title_sort | image segmentation using improved imperialist competitive algorithm and a simple post processing |
| topic | image segmentation clustering improved imperialist competitive algorithm post-processing berkley images dataset |
| url | http://jad.shahroodut.ac.ir/article_1465_2ad4291fcc7695bdd431b925bdb3290a.pdf |
| work_keys_str_mv | AT vnaghashi imagesegmentationusingimprovedimperialistcompetitivealgorithmandasimplepostprocessing AT shlotfi imagesegmentationusingimprovedimperialistcompetitivealgorithmandasimplepostprocessing |