Publication
A discrete labelling approach to attributed graph matching using SIFT features
Conference Article
Conference
International Conference on Pattern Recognition (ICPR)
Edition
20th
Pages
954-957
Doc link
http://dx.doi.org/10.1109/ICPR.2010.239
File
Abstract
Local invariant feature extraction methods are widely used for image-features matching. There exist a number of approaches aimed at the refinement of the matches between image-features. It is a common strategy among these approaches to use geometrical criteria to reject a subset of outliers. One limitation of the outlier rejection design is that it is unable to add new useful matches. We present a new model that integrates the local information of the SIFT descriptors along with global geometrical information to estimate a new robust set of feature-matches. Our approach encodes the geometrical information by means of graph structures while posing the estimation of the feature-matches as a graph matching problem. Some comparative experimental results are presented.
Categories
image recognition, pattern recognition, robot vision.
Scientific reference
G. Sanroma, R. Alquézar Mancho and F. Serratosa i Casanelles. A discrete labelling approach to attributed graph matching using SIFT features, 20th International Conference on Pattern Recognition, 2010, Istanbul, Turkey, pp. 954-957.
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