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Iterative transductive learning for alpha matting

In this paper, we propose a matting algorithm based on iterative transductive learning (for short: ITM). To avoid over-smooth results of recent methods, we introduce the influence of unlabeled regions as well as the consistency of neighboring pixels to re-design the optimization for alpha matting. A novel asymmetric Laplacian matrix is also proposed to further relieve the over-smoothness. To optimize the matting problem, we adjust the constrain coefficients between the initialized alpha matte and the asymmetric Laplacian matrix iteratively to achieve accurate alpha mattes. Consequently, during the iteration, high confidence pixels maintain their refined alpha values, whereas low confidence ones are updated by their neighbors gradually. Experimental results demonstrate that our algorithm is more precise than many state-of-the-art methods in terms of the accuracy.