@inproceedings{4bd6795111a7451bb6935cd2fbfee2b4,
title = "Improved spectral matting by iterative K-means clustering and the modularity measure",
abstract = "Spectral matting is a useful technique for image matting problem. A crucial issue of spectral matting is to determine the number of matting components which has large impacts on the matting performance. In this paper, we propose an improved framework based on spectral matting in order to solve this limitation. Iterative K-means clustering with the assistance of the modularity measure is adopted to obtain the hard segmentation that can be used as the initial guess of soft matting components. The number of matting components can be determined automatically because the improved framework will search possible image components by iteratively dividing image subgraphs.",
keywords = "Image matting, Modularity, Spectral matting",
author = "Wu, {Tung Yu} and Juan, {Hung Hui} and Lu, {Henry Horng Shing}",
year = "2012",
doi = "10.1109/ICASSP.2012.6288094",
language = "English",
isbn = "9781467300469",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "1165--1168",
booktitle = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings",
note = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 ; Conference date: 25-03-2012 Through 30-03-2012",
}