Automatic Image Matting Using Component-Hue-Difference-Based Spectral Matting

摘要

This paper presents automatic image matting using component-hue-difference-based spectral matting to obtain accurate alpha mattes. Spectral matting is the state-of-the-art image matting and it is also a milestone in theoretic matting research. However, the accuracy of alpha matte using spectral matting is usually low without user intervention. In the proposed method, k-means algorithm is used to generate components of a given image. Next, component classification is used based on the hue difference of components to obtain the foreground, background, and unknown components. The corresponding matting components of the foreground, background, and unknown components are obtained via a linear transformation of the smallest eigenvectors of the matting Laplacian matrix. Finally, only matting components of the foreground and unknown components are combined to form the complete alpha matte based on minimizing the matte cost. Experimental results show that the proposed method outperforms the state-of-the-art methods based on spectral matting.