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Graph-Based Representations in Pattern Recognition

Overview of attention for book
Cover of 'Graph-Based Representations in Pattern Recognition'

Table of Contents

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    Book Overview
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    Chapter 1 A One Hour Trip in the World of Graphs, Looking at the Papers of the Last Ten Years
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    Chapter 2 A Unified Framework for Strengthening Topological Node Features and Its Application to Subgraph Isomorphism Detection
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    Chapter 3 On the Complexity of Submap Isomorphism
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    Chapter 4 Flooding Edge Weighted Graphs
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    Chapter 5 Graph Matching with Nonnegative Sparse Model
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    Chapter 6 TurboTensors for Entropic Image Comparison
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    Chapter 7 Active-Learning Query Strategies Applied to Select a Graph Node Given a Graph Labelling
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    Chapter 8 GMTE: A Tool for Graph Transformation and Exact/Inexact Graph Matching
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    Chapter 9 A Comparison of Explicit and Implicit Graph Embedding Methods for Pattern Recognition
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    Chapter 10 Adjunctions on the Lattice of Dendrograms
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    Chapter 11 A Continuous-Time Quantum Walk Kernel for Unattributed Graphs
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    Chapter 12 Relevant Cycle Hypergraph Representation for Molecules
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    Chapter 13 A Quantum Jensen-Shannon Graph Kernel Using the Continuous-Time Quantum Walk
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    Chapter 14 Treelet Kernel Incorporating Chiral Information
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    Chapter 15 A Novel Software Toolkit for Graph Edit Distance Computation
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    Chapter 16 Map Edit Distance vs. Graph Edit Distance for Matching Images
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    Chapter 17 An Algorithm for Maximum Common Subgraph of Planar Triangulation Graphs
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    Chapter 18 Graph Characteristics from the Schrödinger Operator
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    Chapter 19 Persistent Homology in Image Processing
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    Chapter 20 Towards Minimal Barcodes
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    Chapter 21 A Fast Matching Algorithm for Graph-Based Handwriting Recognition
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    Chapter 22 On the Evaluation of Graph Centrality for Shape Matching
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    Chapter 23 Shape Recognition as a Constraint Satisfaction Problem
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    Chapter 24 Gaussian Wave Packet on a Graph
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    Chapter 25 Exact Computation of Median Surfaces Using Optimal 3D Graph Search
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    Chapter 26 Estimation of Distribution Algorithm for the Max-Cut Problem
Overall attention for this book and its chapters
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1 tweeter
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Graph-Based Representations in Pattern Recognition
Published by
Springer, Berlin, Heidelberg, January 2013
DOI 10.1007/978-3-642-38221-5
978-3-64-238220-8, 978-3-64-238221-5

Walter G. Kropatsch, Nicole M. Artner, Yll Haxhimusa, Xiaoyi Jiang

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 94 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 2%
Unknown 92 98%
Readers by discipline Count As %
Computer Science 1 1%
Agricultural and Biological Sciences 1 1%
Unknown 92 98%