Improved spectral matting by iterative K-means clustering and the modularity measure

Tung Yu Wu*, Hung Hui Juan, Henry Horng Shing Lu

*此作品的通信作者

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
頁面1165-1168
頁數4
DOIs
出版狀態Published - 23 10月 2012
事件2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
持續時間: 25 3月 201230 3月 2012

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(列印)1520-6149

Conference

Conference2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
國家/地區Japan
城市Kyoto
期間25/03/1230/03/12

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