Share Email Print

Proceedings Paper

Image feature matching with network flow: a global optimization method
Author(s): Xinying He; Qixiang Ye; Yanmei Liu; Guihong Zhou; Jianbin Jiao
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

A new approach is presented for obtaining feature matching based on the identified features from images. Features which are described by high-dimension vectors are first extracted from a set of reference images and stored in a database, then the correspondence between similar features from different images are established by introducing the notion of a Min-cost K-flow Problem (MKP), which consists in finding a min-cost flow subject to the constraint that the flow value is K. The similarity function, which characterizes these vector components, can avoid the errors that come from different metrics of vectors. Finally, the K-flow is checked to reject ambiguous correspondence bi-directionally and automatically in accordance with the ratio of the matching cost. Experiments on three image sets demonstrate encouraging results.

Paper Details

Date Published: 19 January 2009
PDF: 8 pages
Proc. SPIE 7257, Visual Communications and Image Processing 2009, 72570F (19 January 2009); doi: 10.1117/12.805865
Show Author Affiliations
Xinying He, Graduate Univ. of Chinese Academy of Sciences (China)
Agriculture Univ. of Hebei (China)
Qixiang Ye, Graduate Univ. of Chinese Academy of Sciences (China)
Yanmei Liu, Graduate Univ. of Chinese Academy of Sciences (China)
Guihong Zhou, Agriculture Univ. of Hebei (China)
Jianbin Jiao, Graduate Univ. of Chinese Academy of Sciences (China)

Published in SPIE Proceedings Vol. 7257:
Visual Communications and Image Processing 2009
Majid Rabbani; Robert L. Stevenson, 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?