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Machine Vision and Applications
Erschienen in: Machine Vision and Applications 7/2017

05.06.2017 | Special Issue Paper

Multiple path exploration for graph matching

verfasst von: Ran Chen, Congyan Lang, Tao Wang

Erschienen in: Machine Vision and Applications | Ausgabe 7/2017

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Abstract

The graph matching problem is a hot topic in machine vision. Although a myriad of matching algorithms have been proposed during decades of investigation, it is still a challenging issue because of the combinatorial nature. As one of the outstanding graph matching algorithms, the graduated nonconvexity and concavity procedure follows the path following algorithm. The main drawback of this approach lies that there may exist singular points which violate the smoothness of the solution path and thus harm the accuracy of matching. Addressing this problem, we develop a novel algorithm to bypass this pitfall to improve the matching accuracy. We design an effective method of singular point discovering by checking the smoothness of the path and subsequently explore multiple smooth curves at detected points for better matching results. For evaluation, we make comparisons between our approach and several outstanding matching algorithms on three popular benchmarks, and the results reveal the advantage of our approach.
Vorheriger Artikel Urban 3D segmentation and modelling from street view images and LiDAR point clouds
Nächster Artikel A survey of memory deduplication approaches for intelligent urban computing

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Metadaten
Titel
Multiple path exploration for graph matching
verfasst von
Ran Chen
Congyan Lang
Tao Wang
Publikationsdatum
05.06.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 7/2017
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-017-0847-1

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