| Title | Adaptive video supervoxel segmentation via energy-guided bottom-up clustering |
| Creator | |
| Date Issued | 2025-03-01 |
| Source Publication | Signal, Image and Video Processing
 |
| ISSN | 1863-1703 |
| Volume | 19Issue:3 |
| Abstract | Video supervoxel segmentation is a critical technique in computer vision, facilitating accurate object segmentation and boundary detection in video analysis. Current methods often struggle to balance segmentation accuracy with computational efficiency. This paper proposes an adaptive supervoxel segmentation method for videos, guided by energy-driven bottom-up clustering. By treating each pixel as a potential supervoxel and iteratively merging them based on segmentation energy, our method efficiently generates supervoxels of varying sizes that align well with object boundaries. An optimization algorithm further refines the supervoxels, enhancing shape regularity and boundary smoothness. Extensive comparisons with traditional and deep learning-based methods demonstrate the superior performance of our approach in terms of segmentation accuracy, boundary preservation, and efficiency. The proposed method holds promise for practical applications in video analysis and understanding. |
| Keyword | Clustering Octree Supervoxels Video segmentation |
| DOI | 10.1007/s11760-024-03745-6 |
| URL | View source |
| Language | 英语English |
| Scopus ID | 2-s2.0-85217825397 |
| Citation statistics | |
| Document Type | Journal article |
| Identifier | http://repository.uic.edu.cn/handle/39GCC9TT/12499 |
| Collection | Beijing Normal-Hong Kong Baptist University |
| Corresponding Author | Yang，Baorong |
| Affiliation | 1.Guangdong Provincial/Zhuhai Key Laboratory of IRADS,Beijing Normal University-Hong Kong Baptist University United International College,Zhuhai,China 2.College of Computer Engineering,Jimei University,Xiamen,China 3.School of Informatics,Xiamen University,Xiamen,China 4.Department of Computer Science,The University of Texas at Dallas,Richardson,United States |
| First Author Affilication | Beijing Normal-Hong Kong Baptist University |
| Recommended Citation GB/T 7714 | Dong，Xiao,Zhong，Zhijie,Fan，Wentaoet al. Adaptive video supervoxel segmentation via energy-guided bottom-up clustering[J]. Signal, Image and Video Processing, 2025, 19(3). |
| APA | Dong，Xiao, Zhong，Zhijie, Fan，Wentao, Chen，Zhonggui, Guo，Xiaohu, & Yang，Baorong. (2025). Adaptive video supervoxel segmentation via energy-guided bottom-up clustering. Signal, Image and Video Processing, 19(3). |
| MLA | Dong，Xiao,et al."Adaptive video supervoxel segmentation via energy-guided bottom-up clustering". Signal, Image and Video Processing 19.3(2025). |
| Files in This Item: | There are no files associated with this item. | |||||
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