Image Segmentation using Improved Imperialist Competitive Algorithm and a Simple Post-processing | ||
| Journal of AI and Data Mining | ||
| مقاله 3، دوره 7، شماره 4، بهمن 2019، صفحه 507-519 اصل مقاله (1.76 M) | ||
| نوع مقاله: Research Note | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2019.3935.1464 | ||
| نویسندگان | ||
| V. Naghashi* 1؛ Sh. Lotfi2 | ||
| 1Computer Engineering, University College of Nabi Akram, Rahahan, Tabriz, Iran. | ||
| 2Computer Science, University of Tabriz, Tabriz, Iran. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| image segmentation؛ clustering؛ Improved Imperialist Competitive Algorithm؛ post-processing؛ Berkley images dataset | ||
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آمار تعداد مشاهده مقاله: 692 تعداد دریافت فایل اصل مقاله: 656 |
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