Image feature matching with network flow: a global optimization method

Xinying He; Qixiang Ye; Yanmei Liu; Guihong Zhou; Jianbin Jiao

doi:10.1117/12.805865

19 January 2009 Image feature matching with network flow: a global optimization method

Xinying He, Qixiang Ye, Yanmei Liu, Guihong Zhou, Jianbin Jiao

Proceedings Volume 7257, Visual Communications and Image Processing 2009; 72570F (2009) https://doi.org/10.1117/12.805865
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

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.

Citation Download Citation

Xinying He, Qixiang Ye, Yanmei Liu, Guihong Zhou, and Jianbin Jiao "Image feature matching with network flow: a global optimization method", Proc. SPIE 7257, Visual Communications and Image Processing 2009, 72570F (19 January 2009); https://doi.org/10.1117/12.805865

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