Content-Sensitive Superpixel Generation with Boundary Adjustment
Superpixel segmentation has become a crucial tool in many image processing and computer vision applications. In this paper, a novel content-sensitive superpixel generation algorithm with boundary adjustment is proposed. First, the image local entropy was used to measure the amount of information in...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/9/3150 |
| _version_ | 1827717665066582016 |
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| author | Dong Zhang Gang Xie Jinchang Ren Zhe Zhang Wenliang Bao Xinying Xu |
| author_facet | Dong Zhang Gang Xie Jinchang Ren Zhe Zhang Wenliang Bao Xinying Xu |
| author_sort | Dong Zhang |
| collection | DOAJ |
| description | Superpixel segmentation has become a crucial tool in many image processing and computer vision applications. In this paper, a novel content-sensitive superpixel generation algorithm with boundary adjustment is proposed. First, the image local entropy was used to measure the amount of information in the image, and the amount of information was evenly distributed to each seed. It placed more seeds to achieve the lower under-segmentation in content-dense regions, and placed the fewer seeds to increase computational efficiency in content-sparse regions. Second, the Prim algorithm was adopted to generate uniform superpixels efficiently. Third, a boundary adjustment strategy with the adaptive distance further optimized the superpixels to improve the performance of the superpixel. Experimental results on the Berkeley Segmentation Database show that our method outperforms competing methods under evaluation metrics. |
| first_indexed | 2024-03-10T20:07:36Z |
| format | Article |
| id | doaj.art-8e157fe800604879b1ccae14ee7ffaa4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T20:07:36Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8e157fe800604879b1ccae14ee7ffaa42023-11-19T23:11:03ZengMDPI AGApplied Sciences2076-34172020-04-01109315010.3390/app10093150Content-Sensitive Superpixel Generation with Boundary AdjustmentDong Zhang0Gang Xie1Jinchang Ren2Zhe Zhang3Wenliang Bao4Xinying Xu5College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaTaiyuan Research Institute, China Coal Technology and Engineering Group, Taiyuan 030006, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaSuperpixel segmentation has become a crucial tool in many image processing and computer vision applications. In this paper, a novel content-sensitive superpixel generation algorithm with boundary adjustment is proposed. First, the image local entropy was used to measure the amount of information in the image, and the amount of information was evenly distributed to each seed. It placed more seeds to achieve the lower under-segmentation in content-dense regions, and placed the fewer seeds to increase computational efficiency in content-sparse regions. Second, the Prim algorithm was adopted to generate uniform superpixels efficiently. Third, a boundary adjustment strategy with the adaptive distance further optimized the superpixels to improve the performance of the superpixel. Experimental results on the Berkeley Segmentation Database show that our method outperforms competing methods under evaluation metrics.https://www.mdpi.com/2076-3417/10/9/3150content-sensitivesuperpixelboundary adjustment |
| spellingShingle | Dong Zhang Gang Xie Jinchang Ren Zhe Zhang Wenliang Bao Xinying Xu Content-Sensitive Superpixel Generation with Boundary Adjustment Applied Sciences content-sensitive superpixel boundary adjustment |
| title | Content-Sensitive Superpixel Generation with Boundary Adjustment |
| title_full | Content-Sensitive Superpixel Generation with Boundary Adjustment |
| title_fullStr | Content-Sensitive Superpixel Generation with Boundary Adjustment |
| title_full_unstemmed | Content-Sensitive Superpixel Generation with Boundary Adjustment |
| title_short | Content-Sensitive Superpixel Generation with Boundary Adjustment |
| title_sort | content sensitive superpixel generation with boundary adjustment |
| topic | content-sensitive superpixel boundary adjustment |
| url | https://www.mdpi.com/2076-3417/10/9/3150 |
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