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Graph-Based Representations in Pattern Recognition

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Cover of 'Graph-Based Representations in Pattern Recognition'

Table of Contents

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    Book Overview
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    Chapter 1 Approximation of Graph Edit Distance in Quadratic Time
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    Chapter 2 Data Graph Formulation as the Minimum-Weight Maximum-Entropy Problem
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    Chapter 3 An Entropic Edge Assortativity Measure
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    Chapter 4 Graph-Based Representations in Pattern Recognition
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    Chapter 5 Coupled-Feature Hypergraph Representation for Feature Selection
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    Chapter 6 Reeb Graphs Through Local Binary Patterns
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    Chapter 7 Incremental Embedding Within a Dissimilarity-Based Framework
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    Chapter 8 A First Step Towards Exact Graph Edit Distance Using Bipartite Graph Matching
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    Chapter 9 Consensus of Two Graph Correspondences Through a Generalisation of the Bipartite Graph Matching
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    Chapter 10 Revisiting Volgenant-Jonker for Approximating Graph Edit Distance
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    Chapter 11 A Hypergraph Matching Framework for Refining Multi-source Feature Correspondences
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    Chapter 12 Kite Recognition by Means of Graph Matching
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    Chapter 13 GEM++: A Tool for Solving Substitution-Tolerant Subgraph Isomorphism
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    Chapter 14 A Graph Database Repository and Performance Evaluation Metrics for Graph Edit Distance
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    Chapter 15 Improving Hausdorff Edit Distance Using Structural Node Context
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    Chapter 16 Learning Graph Model for Different Dimensions Image Matching
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    Chapter 17 VF2 Plus: An Improved version of VF2 for Biological Graphs
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    Chapter 18 Report on the First Contest on Graph Matching Algorithms for Pattern Search in Biological Databases
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    Chapter 19 Approximate Graph Edit Distance Computation Combining Bipartite Matching and Exact Neighborhood Substructure Distance
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    Chapter 20 Multi-layer Tree Matching Using HSTs
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    Chapter 21 Large-Scale Graph Indexing Using Binary Embeddings of Node Contexts
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    Chapter 22 Attributed Relational Graph Matching with Sparse Relaxation and Bistochastic Normalization
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    Chapter 23 On the Influence of Node Centralities on Graph Edit Distance for Graph Classification
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    Chapter 24 A Mixed Weisfeiler-Lehman Graph Kernel
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    Chapter 25 A Quantum Jensen-Shannon Graph Kernel Using Discrete-Time Quantum Walks
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    Chapter 26 Density Based Cluster Extension and Dominant Sets Clustering
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    Chapter 27 Salient Object Segmentation from Stereoscopic Images
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    Chapter 28 Causal Video Segmentation Using Superseeds and Graph Matching
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    Chapter 29 Fast Minimum Spanning Tree Based Clustering Algorithms on Local Neighborhood Graph
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    Chapter 30 From Bags to Graphs of Stereo Subgraphs in Order to Predict Molecule’S Properties
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    Chapter 31 Thermodynamics of Time Evolving Networks
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    Chapter 32 Isometric Mapping Hashing
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    Chapter 33 Skeletal Graphs from Schrödinger Magnitude and Phase
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    Chapter 34 Graph Based Lymphatic Vessel Wall Localisation and Tracking
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    Chapter 35 A Comic Retrieval System Based on Multilayer Graph Representation and Graph Mining
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    Chapter 36 Learning High-Order Structures for Texture Retrieval
Attention for Chapter 36: Learning High-Order Structures for Texture Retrieval
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Chapter title
Learning High-Order Structures for Texture Retrieval
Chapter number 36
Book title
Graph-Based Representations in Pattern Recognition
Published in
Lecture notes in computer science, January 2015
DOI 10.1007/978-3-319-18224-7_36
Book ISBNs
978-3-31-918223-0, 978-3-31-918224-7
Authors

Liu, Ni, Gimel’farb, Georgy, Delmas, Patrice, Ni Liu, Georgy Gimel’farb, Patrice Delmas