Journal ArticleDOI
Approximate graph edit distance computation by means of bipartite graph matching
Kaspar Riesen,Horst Bunke +1 more
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TLDR
A novel algorithm is introduced which allows us to approximately, or suboptimally, compute edit distance in a substantially faster way and is emprically verified that the accuracy of the suboptimal distance remains sufficiently accurate for various pattern recognition applications.About:
This article is published in Image and Vision Computing.The article was published on 2009-06-01. It has received 654 citations till now. The article focuses on the topics: Graph operations & Line graph.read more
Citations
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Journal ArticleDOI
Intelligent and automatic wavelength calibration method.
TL;DR: The matching method, combining the Hough transform and random sample consensus, can effectively solve the matching problem in wavelength calibration without manual intervention and the average accuracy and average recall rate exceed the traditional manual matching method and maximum linear matching method.
Book ChapterDOI
Metric Indexing for Graph Similarity Search
TL;DR: In this article, a filter-verification framework for similarity search is proposed, which supports non-uniform edit costs for graphs with arbitrary attributes and satisfies the triangle inequality, making it suitable for acceleration by metric indexing.
Recognize multi-touch gestures by graph modeling and matching
TL;DR: Results prove that the new generic graph model presented can be fruitfully used for multi-touch gesture pattern recognition purpose with the classifier of graph embedding and SVM.
Dissertation
Predicting query performance and explaining results to assist Linked Data consumption
TL;DR: In this article, the authors present a machine learning approach to predict query performance based on the query execution history, which is suitable for the Linked Data scenario where statistics about the underlying data is often missing such as when data is controlled by external parties.
Journal ArticleDOI
A general model to define the substitution, insertion and deletion graph edit costs based on an embedded space
TL;DR: The main feature of the method is that the insertion, deletion and substitution costs are learned together, training the same machine learning and generating only one model, achieving a higher accuracy in the pattern recognition stage than previous methods.
References
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Journal ArticleDOI
The Protein Data Bank
Helen M. Berman,John D. Westbrook,Zukang Feng,Gary L. Gilliland,Talapady N. Bhat,Helge Weissig,Ilya N. Shindyalov,Philip E. Bourne +7 more
TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.
Journal ArticleDOI
The Hungarian method for the assignment problem
TL;DR: This paper has always been one of my favorite children, combining as it does elements of the duality of linear programming and combinatorial tools from graph theory, and it may be of some interest to tell the story of its origin this article.
Journal ArticleDOI
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
TL;DR: How heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching is described and an optimality property of a class of search strategies is demonstrated.
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