Improving sampling-based image matting with cooperative coevolution differential evolution algorithm

• Published in: Soft computing (Berlin, Germany) Germany), 2017-08, Vol.21 (15), p.4417-4430
• Main Authors: Cai, Zhao-Quan, Lv, Liang, Huang, Han, Hu, Hui, Liang, Yi-Hui
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Computational Intelligence; Control; Engineering; Evolutionary algorithms; Evolutionary computation; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Methods; Operators (mathematics); Optimization; Pixels; Robotics; Sampling; Searching
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-016-2250-7

Image matting is a fundamental operator in image editing and has significant influence on video production. This paper explores sampling-based image matting technology, with the aim to improve the accuracy of matting result. The result of sampling-based image matting technology is determined by the selected samples. Every undetermined pixel needs both a foreground and background pixel to estimate whether the undetermined one is in the foreground region of the image. These foreground pixels and background pixels are sampled from known regions, which form sample pairs. High-quality sample pairs can improve the accuracy of matting results. Therefore, how to search for the best sample pairs for all undetermined pixels is a key optimization problem of sampling-based image matting technology, termed “sample optimization problem.” In this paper, in order to improve the efficiency of searching for high-quality sample pairs, we propose a cooperative coevolution differential evolution (DE) algorithm in solution to this optimization problem. Strong-correlate pixels are divided into a group to cooperatively search for the best sample pairs. In order to avoid premature convergence of DE algorithm, a scattered strategy is used to keep the diversity of population. Besides, a simple but effective evaluation function is proposed to distinguish the quality of various candidate solutions. The existing optimization method, original DE algorithm and a popular evolution algorithm are used for comparison. The experimental results demonstrate that the proposed cooperative coevolution DE algorithm can search for higher-quality sample pairs and improve the accuracy of sampling-based image matting.