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A Comprehensive Survey on Sampling‐Based Image Matting
Author(s) -
Yao Guilin,
Zhao Zhijie,
Liu Shaohui
Publication year - 2017
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13156
Subject(s) - sampling (signal processing) , computer science , complement (music) , field (mathematics) , process (computing) , artificial intelligence , computer vision , mathematics , biochemistry , chemistry , filter (signal processing) , complementation , pure mathematics , gene , phenotype , operating system
Sampling‐based image matting is currently playing a significant role and showing great further development potentials in image matting. However, the consequent survey articles and detailed classifications are still rare in the field of corresponding research. Furthermore, besides sampling strategies, most of the sampling‐based matting algorithms apply additional operations which actually conceal their real sampling performances. To inspire further improvements and new work, this paper makes a comprehensive survey on sampling‐based matting in the following five aspects: (i) Only the sampling step is initially preserved in the matting process to generate the final alpha results and make comparisons. (ii) Four basic categories including eight detailed classes for sampling‐based matting are presented, which are combined to generate the common sampling‐based matting algorithms. (iii) Each category including two classes is analysed and experimented independently on their advantages and disadvantages. (iv) Additional operations, including sampling weight, settling manner, complement and pre‐ and post‐processing, are sequentially analysed and added into sampling. Besides, the result and effect of each operation are also presented. (v) A pure sampling comparison framework is strongly recommended in future work.