Pixel-Level Discrete Multiobjective Sampling for Image Matting

• Published in: IEEE transactions on image processing Vol. 28; no. 8; pp. 3739 - 3751
• Main Authors: Huang, Han, Liang, Yihui, Yang, Xiaowei, Hao, Zhifeng
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-08-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Color; color sampling; Estimation; Image color analysis; image matting; Indexes; Linear programming; Mattes; Mopping; Multiobjective sampling; Multiple objective analysis; Optimization; Pareto optimization; Pixels; Polydimethylsiloxane; Sampling methods; Silicone resins
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2019.2902830

In sampling-based matting methods, the alpha is estimated by choosing the best pair of foreground and background color samples. The lack of true samples is the major obstacle in obtaining high-quality alpha mattes. Regrettably, several proposed approaches did not address the conflicts among multiple sampling criteria and the effects of incomplete sample spaces. To address this issue, we propose a pixel-level discrete multiobjective sampling (PDMS) method. The color sampling process at each unknown pixel is formalized as a multiobjective optimization problem (MOP). The strength of PDMS includes its ability to minimize both color difference and spatial distance between unknown and known pixels, and its capacity to adaptively make trade-offs among conflicting sampling criteria. To mitigate the effects of incomplete sample spaces, the sample space is extended to complete known regions in PDMS, which means that the colors of all known pixels can be sampled, instead of mean colors of superpixels. Our experimental results show that PDMS collects a small set of samples while achieving smaller minimum absolute difference in alpha estimation. Moreover, PDMS implements pixel-level sampling by using the proposed multiobjective optimization algorithm to efficiently solve sampling MOPs. The PDMS-based matting method provides high-quality alpha mattes with sharp boundaries and thus outperforms those prior image matting methods in terms of gradient error.