{"id":3151,"date":"2026-04-10T10:50:44","date_gmt":"2026-04-10T02:50:44","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=3151"},"modified":"2026-06-10T15:27:01","modified_gmt":"2026-06-10T07:27:01","slug":"boundary-preserving-superpixel-segmentation","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=boundary-preserving-superpixel-segmentation","title":{"rendered":"Boundary-preserving superpixel segmentation"},"content":{"rendered":"\n
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<\/i> 2024<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 27, Issue 4<\/a><\/p>\n<\/div>\n\n\n\n

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Yuejia Lin, Zhao Li, Chenxun Yuan, and Yi Liu<\/i><\/a><\/p>\n\n\n\n

School of Software, Shandong University, Jinan 250101, China<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0March 23, 2023
Accepted:\u00a0May 9, 2023
Publication Date:\u00a0April 10, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Normalization function.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202404_27(4).0002<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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In recent years, superpixel segmentation has been widely used in image processing tasks as a preprocessing step. Superpixel segmentation aims to group pixels into homogeneous regions while maintaining edges. This paper proposes a superpixel segmentation algorithm based on boundary preservation. In the algorithm, the side window filtering is first used to smooth the image texture area, so that the superpixel shape generated at the texture is regular. Different from other superpixel clustering algorithms, the algorithm in this paper uses a new distance measurement function for distance measurement, which can assign different weights to its color distance items and spatial distance items according to different pixels, so that the superpixel fit in the image boundary area. The boundary is regular in the flat area. The distance measurement function also takes into account the pixel information of the linear path from the pixel to the cluster center, and avoids the category error division caused by only the local information of the pixel for clustering. Finally, this paper designs a new cluster center update strategy, which uses only the weighted average of some reliable pixels in the superpixel as the new cluster center, thereby reducing the update of the cluster center of pixels that are not very similar to the cluster center. The interference makes the cluster center update more accurate. Experimental results show that our algorithm can get better results in visual effects and BR,UE,ASA indicators compared with existing algorithms.<\/p>\n\n\n\n

Keywords:\u00a0Superpixel segmentation; Clustering; superpixels; Image boundaries; Image segmentation<\/em><\/p>\n\n\n\n

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