Tensorial Evolutionary Optimization for Natural Image Matting
- Title
- Tensorial Evolutionary Optimization for Natural Image Matting
- Author
- Jun Zhang; Gong, Yue-Jiao; Xiao, Xiao-Lin; Zhou, Yi-Cong; Zhang, Jun
- Keywords
- natural image matting; tensorial evolutionary algorithm; heuristic optimization
- Issue Date
- 2024-03-27
- Publisher
- Association for Computing Machinery (ACM)
- Citation
- ACM Transactions on Multimedia Computing, Communications and Applications
- 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.
- URI
- https://dl.acm.org/doi/full/10.1145/3649138https://repository.hanyang.ac.kr/handle/20.500.11754/189527
- ISSN
- 1551-6857; 1551-6865
- DOI
- 10.1145/3649138
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
- Files in This Item:
- 3649138.pdfDownload
- Export
- RIS (EndNote)
- XLS (Excel)
- XML