A cluster sampling method for image matting via sparse coding

In this paper, we present a new image matting algorithm which solves two major problems encountered by previous samplingbased algorithms. The first is that existing sampling-based approaches typically rely on certain spatial assumptions in collecting samples from known regions, and thus their performance deteriorates if the underlying assumptions are not satisfied. Here, we propose a method that a more representative set of samples is collected so as not to miss out true samples. This is accomplished by clustering the foreground and background pixels and collecting samples from each of the clusters. The second problem is that the quality of matting result is determined by the goodness of a single sample pair which causes errors when sampling-based methods fail to select the best pairs. In this paper, we derive a new objective function for directly obtaining the estimation of the alpha matte from a bunch of samples. Comparison on a standard benchmark dataset demonstrates that the proposed approach generates more robust and accurate alpha matte than state-of-the-art methods.