SALIENCY-BASED UNSUPERVISED IMAGE MATTING
Guanghua Tan, JUN QI, Chunming Gao, Jin Chen, Liyuan Zhuo · International Journal of Pattern Recognition and Artificial Intelligence · 2014
Spectral matting is the state-of-the-art matting method and can well solve the highly under-conditioned matte problem without manual intervention. However, it suffers from huge computation cost and inaccurate alpha matte. This paper presents a modified spectral matting method which combines saliency detection algorithm to get a higher accuracy of alpha matte with less computational cost. First, the saliency detection algorithm is used to detect general locations of foreground objects. For saliency detection method, original two-stage scheme is replaced by feedback scheme to get a more suitable saliency map for unsupervised image matting. Next, matting components are obtained through a linear transformation of the smallest eigenvectors of the matting Laplacian matrix. Then, the improved saliency map is used for grouping matting components. Finally, the alpha matte is obtained based on matte cost function. Experiments show that the proposed method outperforms the state-of-the-art methods based on spectral matting both in speed and alpha matte accuracy.