Graph-Based Representations in Pattern Recognition

1. Front Matter

   Pages I-XV

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2. Image and Shape Analysis

   1. Front Matter

      Pages 1-1

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   2. Saliency Detection via A Graph Based Diffusion Model

      • Zhouqin He, Bo Jiang, Yun Xiao, Chris Ding, Bin Luo

      Pages 3-12

   3. Shape Simplification Through Graph Sparsification

      • Francisco Escolano, Manuel Curado, Silvia Biasotti, Edwin R. Hancock

      Pages 13-22

   4. Reeb Graphs of Piecewise Linear Functions

      • Barbara Di Fabio, Claudia Landi

      Pages 23-35

3. Learning and Graph Kernels

   1. Front Matter

      Pages 37-37

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   2. Learning from Diffusion-Weighted Magnetic Resonance Images Using Graph Kernels

      • Sylvain Takerkart, Gottfried Berton, Nicole Malfait, François-Xavier Dupé

      Pages 39-48

   3. Learning Graph Matching with a Graph-Based Perceptron in a Classification Context

      • Romain Raveaux, Chloé Martineau, Donatello Conte, Gilles Venturini

      Pages 49-58

   4. A Nested Alignment Graph Kernel Through the Dynamic Time Warping Framework

      • Lu Bai, Luca Rossi, Lixin Cui, Edwin R. Hancock

      Pages 59-69

4. Graph Applications

   1. Front Matter

      Pages 71-71

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   2. GERoMe – A Novel Graph Extraction Robustness Measure

      • Dominik Drees, Aaron Scherzinger, Xiaoyi Jiang

      Pages 73-82

   3. Speeding-Up Graph-Based Keyword Spotting in Historical Handwritten Documents

      • Michael Stauffer, Andreas Fischer, Kaspar Riesen

      Pages 83-93

   4. Detecting Alzheimer’s Disease Using Directed Graphs

      • Jianjia Wang, Richard C. Wilson, Edwin R. Hancock

      Pages 94-104

5. Graph Matching

   1. Front Matter

      Pages 105-105

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   2. Error-Tolerant Coarse-to-Fine Matching Model for Hierarchical Graphs

      • Pau Riba, Josep Lladós, Alicia Fornés

      Pages 107-117

   3. A Hungarian Algorithm for Error-Correcting Graph Matching

      • Sébastien Bougleux, Benoit Gaüzère, Luc Brun

      Pages 118-127

   4. Introducing VF3: A New Algorithm for Subgraph Isomorphism

      • Vincenzo Carletti, Pasquale Foggia, Alessia Saggese, Mario Vento

      Pages 128-139

6. Large Graphs and Social Networks

   1. Front Matter

      Pages 141-141

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   2. Node Matching Computation Between Two Large Graphs in Linear Computational Cost

      • Pep Santacruz, Shaima Algabli, Francesc Serratosa

      Pages 143-153

   3. Measuring Vertex Centrality Using the Holevo Quantity

      • Luca Rossi, Andrea Torsello

      Pages 154-164

This book constitutes the refereed proceedings of the 11th IAPR-TC-15 International Workshop on Graph-Based Representation in Pattern Recognition, GbRPR 2017, held in Anacapri, Italy, in May 2017. The 25 full papers and 2 abstracts of invited papers presented in this volume were carefully reviewed and selected from 31 submissions. The papers  discuss research results and applications in the intersection of pattern recognition, image analysis, graph theory, and also the application of graphs to pattern recognition problems in other fields like computational topology, graphic recognition systems and bioinformatics.

• complex networks
• dynamic networks
• graph algorithms
• graph matching
• optimization
• bipartite graph matching
• clustering
• financial markets
• graph classification
• graph clustering
• graph edit distance
• graph kernels
• graph-based imagaeand shape analysis
• graph-based representations
• large graphs and social network analysis
• magnetic resonance imaging
• robustness
• saliency detection
• structural pattern recognition
• data structures
• algorithm analysis and problem complexity

• Università degli Studi di Salerno, Fisciano, Italy

  Pasquale Foggia, Mario Vento

• Chinese Academy of Sciences, Beijing, China

  Cheng-Lin Liu

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