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
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Posted Content
GLAMOUR: Graph Learning over Macromolecule Representations
TL;DR: GLAMOUR as mentioned in this paper is a framework for chemistry-informed graph representation of macromolecules that enables quantifying structural similarity, and interpretable supervised learning for macromolescules.
Object Detection and Recognition for Visually Impaired People
TL;DR: This project employs in deep learning a deep Neural Network (DNN) for recognizing the object which is captured from the real world and the name of the object is converted into audio output with the help of gTTS.
Proceedings ArticleDOI
Recognition of natural scene images based on graphs
Qiangrong Jiang,Weina Wei +1 more
TL;DR: This paper proposes to design binary classifiers capable to recognize some generic natural scene images, the countryside class and the city class for instance, and proposes a new simple kernal function based on graph edit distance and raises the question that Munkres' algorithm can be used to measure the similarity between the images.
MonographDOI
Computational Approaches to the Network Science of Teams
Liangyue Li,Hanghang Tong +1 more
TL;DR: In this paper, a comprehensive review for researchers and practitioners in data mining and social networks surveys recent progress in the emerging field of network science of teams and presents models and algorithms characterizing, predicting, optimizing, and explaining team performance, along with key applications, open challenges, and future trends.
Journal ArticleDOI
Structural Data Recognition With Graph Model Boosting
Tomo Miyazaki,Shinichiro Omachi +1 more
TL;DR: A large number of graph models are constructed and a strong classifier is trained using the models in a boosting framework, which can perform structural data recognition with powerful recognition capability in the face of comprehensive structural variation.
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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