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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

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Approximation of Graph Edit Distance in Quadratic Time
  3. Altmetric Badge
    Chapter 2 Data Graph Formulation as the Minimum-Weight Maximum-Entropy Problem
  4. Altmetric Badge
    Chapter 3 An Entropic Edge Assortativity Measure
  5. Altmetric Badge
    Chapter 4 Graph-Based Representations in Pattern Recognition
  6. Altmetric Badge
    Chapter 5 Coupled-Feature Hypergraph Representation for Feature Selection
  7. Altmetric Badge
    Chapter 6 Reeb Graphs Through Local Binary Patterns
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    Chapter 7 Incremental Embedding Within a Dissimilarity-Based Framework
  9. Altmetric Badge
    Chapter 8 A First Step Towards Exact Graph Edit Distance Using Bipartite Graph Matching
  10. Altmetric Badge
    Chapter 9 Consensus of Two Graph Correspondences Through a Generalisation of the Bipartite Graph Matching
  11. Altmetric Badge
    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
  16. Altmetric Badge
    Chapter 15 Improving Hausdorff Edit Distance Using Structural Node Context
  17. Altmetric Badge
    Chapter 16 Learning Graph Model for Different Dimensions Image Matching
  18. Altmetric Badge
    Chapter 17 VF2 Plus: An Improved version of VF2 for Biological Graphs
  19. Altmetric Badge
    Chapter 18 Report on the First Contest on Graph Matching Algorithms for Pattern Search in Biological Databases
  20. Altmetric Badge
    Chapter 19 Approximate Graph Edit Distance Computation Combining Bipartite Matching and Exact Neighborhood Substructure Distance
  21. Altmetric Badge
    Chapter 20 Multi-layer Tree Matching Using HSTs
  22. Altmetric Badge
    Chapter 21 Large-Scale Graph Indexing Using Binary Embeddings of Node Contexts
  23. Altmetric Badge
    Chapter 22 Attributed Relational Graph Matching with Sparse Relaxation and Bistochastic Normalization
  24. Altmetric Badge
    Chapter 23 On the Influence of Node Centralities on Graph Edit Distance for Graph Classification
  25. Altmetric Badge
    Chapter 24 A Mixed Weisfeiler-Lehman Graph Kernel
  26. Altmetric Badge
    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
  28. Altmetric Badge
    Chapter 27 Salient Object Segmentation from Stereoscopic Images
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    Chapter 28 Causal Video Segmentation Using Superseeds and Graph Matching
  30. Altmetric Badge
    Chapter 29 Fast Minimum Spanning Tree Based Clustering Algorithms on Local Neighborhood Graph
  31. Altmetric Badge
    Chapter 30 From Bags to Graphs of Stereo Subgraphs in Order to Predict Molecule’S Properties
  32. Altmetric Badge
    Chapter 31 Thermodynamics of Time Evolving Networks
  33. Altmetric Badge
    Chapter 32 Isometric Mapping Hashing
  34. Altmetric Badge
    Chapter 33 Skeletal Graphs from Schrödinger Magnitude and Phase
  35. Altmetric Badge
    Chapter 34 Graph Based Lymphatic Vessel Wall Localisation and Tracking
  36. Altmetric Badge
    Chapter 35 A Comic Retrieval System Based on Multilayer Graph Representation and Graph Mining
  37. Altmetric Badge
    Chapter 36 Learning High-Order Structures for Texture Retrieval
Overall attention for this book and its chapters
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Mentioned by

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1 X user

Citations

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2 Dimensions

Readers on

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29 Mendeley
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Title
Graph-Based Representations in Pattern Recognition
Published by
Lecture notes in computer science, January 2015
DOI 10.1007/978-3-319-18224-7
ISBNs
978-3-31-918223-0, 978-3-31-918224-7
Authors

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

Editors

Cheng, Jian, Kropatsch, Walter G., Luo, Bin, Liu, Cheng-Lin

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 3%
Student > Master 1 3%
Unknown 27 93%
Readers by discipline Count As %
Economics, Econometrics and Finance 1 3%
Unknown 28 97%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 April 2016.
All research outputs
#15,431,277
of 22,940,083 outputs
Outputs from Lecture notes in computer science
#4,649
of 8,129 outputs
Outputs of similar age
#209,785
of 353,829 outputs
Outputs of similar age from Lecture notes in computer science
#164
of 257 outputs
Altmetric has tracked 22,940,083 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,129 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 353,829 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 257 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.