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| An Alpha Matting Algorithm Based on Micro-scale Searching for High-Resolution Images |
| FENG Fujian1,2, YANG Yuan1,2, TAN Mian1,2, GOU Hongshan1,2, LIANG Yihui3, WANG Lin1,2 |
1. College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025;
2. Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province, Guizhou Minzu University, Guiyang 550025;
3. School of Computer Engineering, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528400 |
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Abstract High-resolution image matting is essentially a large-scale combinatorial optimization for foreground/background pixel pairs. However, there is few research achievements on this issue. Alpha matte inverse extraction occurs when the foreground and the background in an image are highly similar. To address this problem, a decision set decomposition strategy is designed to effectively decompose high-resolution image matting problems. Moreover, a optimized information transmission strategy is designed, the weight relationship between sub-problems is obtained, and the optimization sequence of the image matting problem is established. Based on the optimized information transmission strategy, an alpha matting algorithm based on micro-scale searching(MS-AM) is proposed. MS-AM effectively solves the issue of alpha matte inverse extraction in high-resolution image matting problems by searching through effective decision subsets instead of the entire decision set, providing insights for the analysis of large-scale combinatorial optimization problems. The alphamatting benchmark dataset is selected as testing data, and MS-AM is compared with typical matting optimization algorithms. Results demonstrate that MS-AM can address alpha matte inverse extraction problem when the foreground is similar to the background and improve the alpha matting accuracy with significantly reduced dimension of high-resolution image matting problem.
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Received: 27 February 2023
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| Fund:National Natural Science Foundation of China(No.62002053), the Science and Technology Program of Guizhou Province(No.QKH-Basic-ZK[2022]195,QKH-Basic-ZK[2023]143,QKH-Platform Talent-ZCKJ[2021]007), the Youth Science and Technology Talent Growth Project of Guizhou Province(No.QJH-KY[2021]104,QJH-KY[2021]111), the Natural Science Research Project of Education Department of Guizhou Province(No.QJJ2023012,QJJ2022015,QJJ2023061,QJJ2023062), the "Teaching Process Quality Evaluation" Pilot Project of Deepening Education Evaluation Reform in the New Era in Guizhou Province, Guangdong Basic and Applied Basic Research Foundation(No.2019A1515111082), the Science and Technology Foundation of Guangdong Province(No.2021A0101180005), the Social Welfare Science and Technology Research Project of Zhongshan City(No.2019B2010,210714094038458), the Key R&D Project of Zhongshan City(No.2019A4018) |
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Corresponding Authors:
TAN Mian, ma-ster, associate professor. Her research inte-rests include natural image matting and micro-computation.
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| About author:: About Author:FENG Fujian, Ph.D., professor. His research interests include evolutionary computation and micro-computation.YANG Yuan, master student. Her research interests include natural image matting.GOU Hongshan, master student. His research interests include alpha matting and image processing.LIANG Yihui, Ph.D., associate profe-ssor. His research interests include image matting and image processing.WANG Lin, Ph.D., professor. His research interests include image processing and pattern recognition. |
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2023, Vol. 36 
