Share Email Print

Proceedings Paper

Interactive image segmentation by constrained spectral graph partitioning
Author(s): Hao Zhang; Jin He; Hong Zhang; Zhanhua Huang
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

This paper proposed an interactive image segmentation algorithm that can tolerate slightly incorrect user constraints. Interactive image segmentation was formulated as a constrained spectral graph partitioning problem. Furthermore, it was proven to equal to a supervised classification problem, where the feature space was formed by rows of the eigenvector matrix that was computed by spectral graph analysis. ν-SVM (support vector machine) was preferred as the classifier. Some incorrect labels in user constraints were tolerated by being identified as margin errors in ν-SVM. Comparison with other algorithms on real color images was reported.

Paper Details

Date Published: 10 November 2010
PDF: 6 pages
Proc. SPIE 7850, Optoelectronic Imaging and Multimedia Technology, 78501X (10 November 2010); doi: 10.1117/12.870254
Show Author Affiliations
Hao Zhang, Tianjin Univ. (China)
Jin He, Tianjin Univ. of Technology and Education (China)
Hong Zhang, Univ. of Alberta (Canada)
Zhanhua Huang, Tianjin Univ. (China)

Published in SPIE Proceedings Vol. 7850:
Optoelectronic Imaging and Multimedia Technology
Toru Yoshizawa; Ping Wei; Jesse Zheng; Tsutomu Shimura, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?