Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jun Zhang | - |
dc.contributor.author | Gong, Yue-Jiao | - |
dc.contributor.author | Xiao, Xiao-Lin | - |
dc.contributor.author | Zhou, Yi-Cong | - |
dc.contributor.author | Zhang, Jun | - |
dc.date.accessioned | 2024-04-01T02:47:32Z | - |
dc.date.available | 2024-04-01T02:47:32Z | - |
dc.date.issued | 2024-03-27 | - |
dc.identifier.citation | ACM Transactions on Multimedia Computing, Communications and Applications | en_US |
dc.identifier.issn | 1551-6857 | en_US |
dc.identifier.issn | 1551-6865 | en_US |
dc.identifier.uri | https://dl.acm.org/doi/full/10.1145/3649138 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/189527 | - |
dc.description.abstract | Natural image matting has garnered increasing attention in various computer vision applications. The matting problem aims to find the optimal foreground/background (F/B) color pair for each unknown pixel and thus obtain an alpha matte indicating the opacity of the foreground object. This problem is typically modeled as a large-scale pixel pair combinatorial optimization (PPCO) problem. Heuristic optimization is widely employed to tackle the PPCO problem owing to its gradient-free property and promising search ability. However, traditional heuristic methods often encode F/B solutions to a one-dimensional (1D) representation and then evolve the solutions in a 1D manner. This 1D representation destroys the intrinsic two-dimensional (2D) structure of images, where the significant spatial correlations among pixels are ignored. Moreover, the 1D representation also brings operation inefficiency. To address the above issues, this article develops a spatial-aware tensorial evolutionary image matting (TEIM) method. Specifically, the matting problem is modeled as a 2D Spatial-PPCO (S-PPCO) problem, and a global tensorial evolutionary optimizer is proposed to tackle the S-PPCO problem. The entire population is represented as a whole by a third-order tensor, in which individuals are classified into two types: F and B individuals for denoting the 2D F/B solutions, respectively. The evolution process, consisting of three tensorial evolutionary operators, is implemented based on pure tensor computation for efficiently seeking F/B solutions. The local spatial smoothness of images is also integrated into the evaluation process for obtaining a high-quality alpha matte. Experimental results compared with state-of-the-art methods validate the effectiveness of TEIM. | en_US |
dc.language | en_US | en_US |
dc.publisher | Association for Computing Machinery (ACM) | en_US |
dc.relation.ispartofseries | Volume 20, Issue 7;1-14 | - |
dc.subject | natural image matting | en_US |
dc.subject | tensorial evolutionary algorithm | en_US |
dc.subject | heuristic optimization | en_US |
dc.title | Tensorial Evolutionary Optimization for Natural Image Matting | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1145/3649138 | en_US |
dc.relation.page | 1-14 | - |
dc.relation.journal | ACM Transactions on Multimedia Computing, Communications and Applications | - |
dc.contributor.googleauthor | Lei, Si-chao | - |
dc.relation.code | 2024017835 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | SCHOOL OF ELECTRICAL ENGINEERING | - |
dc.identifier.pid | junzhanghk | - |
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