A Comprehensive Survey on Sampling‐Based Image Matting

Abstract

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.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.