[1]CHEN Qiufeng,SHEN Quntai.Random-walk matting with local adaptive input control[J].CAAI Transactions on Intelligent Systems,2019,14(5):1007-1016.[doi:10.11992/tis.201809014]
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Random-walk matting with local adaptive input control

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 5 Page number: 1007-1016 Column: 学术论文—人工智能基础 Public date: 2019-09-05
Title:
Random-walk matting with local adaptive input control
Author(s):
CHEN Qiufeng1; 2;  SHEN Quntai2
1. College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China;
2. School of Information Science and Engineering, Central South University, Changsha 410083, China
Keywords:
matting; video matting; random walk; soft constrained; absorption probability; local model; input control; adaptive control
CLC:
TP391
DOI:
10.11992/tis.201809014
Abstract:
In traditional image-matting algorithms, incomplete user labeling and inaccurate super-pixel segmentation lead to the incorrect propagation of information. To solve this problem, we propose the use of soft constrained matting based on a random-walk algorithm. Through the derivation of the extended Dirichlet problem, we identify the relationship between a random walk with soft constraint and the probability of a partial self-absorption random walk. Guided by the absorption probability, an input control matrix is designed according to the rank and variance of the feature matrix in the local window. This is performed via a graph model that was constructed using traditional similarity diffusion such that the process of information diffusion could follow the local image features to realize adaptive diffusion. Finally, we applied the soft constrained random walk to single-frame two-layer image and video matting. The experimental results reveal that the proposed algorithm can transmit information over long distances and has good fault tolerance. In addition, it can achieve better image matting results, particularly in cases wherein user labeling is insufficient.
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