Publication

Attributed graph matching for image-features association using SIFT descriptors

Conference Article

Conference

Joint IAPR International Workshop on Structural, Syntactic and Statistical Pattern Recognition (SSPR&SPR)

Edition

13th

Pages

254-263

Doc link

http://dx.doi.org/10.1007/978-3-642-14980-1_24

File

Download the digital copy of the doc pdf document

Abstract

Image-features matching based on SIFT descriptors is subject to the misplacement of certain matches due to the local nature of the SIFT representations. Some well-known outlier rejectors aim to remove those misplaced matches by imposing geometrical consistency. We present two graph matching approaches (one continuous and one discrete) aimed at the matching of SIFT features in a geometrically consistent way. The two main novelties are that, both local and contextual coherence are imposed during the optimization process and, a model of structural consistency is presented that accounts for the quality rather than the quantity of the surrounding matches. Experimental results show that our methods achieve good results under various types of noise.

Categories

image recognition, pattern recognition, robot vision.

Author keywords

attributed graph matching, SIFT, image registration, discrete labeling, softassign

Scientific reference

G. Sanroma, R. Alquézar Mancho and F. Serratosa i Casanelles. Attributed graph matching for image-features association using SIFT descriptors, 13th Joint IAPR International Workshop on Structural, Syntactic and Statistical Pattern Recognition, 2010, Izmir, Turkey, in Structural, Syntactic, and Statistical Pattern Recognition, Vol 6218 of Lecture Notes in Computer Science, pp. 254-263, 2010, Springer, Berlin.