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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.

In traditional image segmentation, the GrabCut image segmentation algorithm is a popular and effective method. The current GrabCut image segmentation algorithm is based on the Gaussian mixture model of the global foreground and background of the image. Still, it cannot achieve good results when the foreground and background are similar. In this paper, we propose a method for local sampling of the foreground and background. This method samples the foreground and background around unknown pixels based on the distance. It has an advantage when the foreground and background are similar. The experimental results show that the GrabCut method with local sampling can achieve good results when many colors appear in the foreground and background at the same time.

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Up to now analytical or statistical methods have been used in sign language recognition with large vocabulary. Analytical methods such as Dynamic Time Wrapping (DTW) or Euclidian distance have been used for isolated word recognition, but the performance is not satisfactory enough because it is easily interfered by noise. Statistical methods, especially hidden Markov Models are commonly used, for both continuous sign language and isolated words and with the expansion of vocabulary the processing time becomes increasingly unacceptable. Therefore, a multilayer architecture of sign language recognition for large vocabulary is proposed in this paper for the purpose of speeding up the recognition process. In this method the gesture sequence to be recognized is first located at a set of words that are easy to be confused (confusion set) through a global cursory search and then the gesture is recognized through a latter local search and the generation of confusion set is realized by DTW/ISODATA algorithm.