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" Graph-based representations in pattern recognition : "
Donatello Conte, Jean-Yves Ramel, Pasquale Foggia (eds.).
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BL
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862102
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| Main Entry
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IAPR-TC15 Workshop on Graph-Based Representations in Pattern Recognition(12th :2019 :, Tours, France)
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| Title & Author
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Graph-based representations in pattern recognition : : 12th IAPR-TC-15 International Workshop, GbRPR 2019, Tours, France, June 19-21, 2019, Proceedings /\ Donatello Conte, Jean-Yves Ramel, Pasquale Foggia (eds.).
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| Publication Statement
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Cham, Switzerland :: Springer,, 2019.
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| Series Statement
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Lecture notes in computer science ;; 11510
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LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics
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1 online resource (x, 247 pages) :: illustrations (some color)
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| ISBN
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3030200809
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: 3030200817
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: 3030200825
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: 9783030200800
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: 9783030200817
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: 9783030200824
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9783030200800
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| Notes
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Includes author index.
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| Contents
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Intro; Preface; Organization; Contents; Experimental Evaluation of Subgraph Isomorphism Solvers; 1 Introduction; 2 Experimental Set-Up; 3 Does the Solving Time Depend on Graph Sizes?; 4 Where Are the Hard Instances?; 5 Experimental Comparison of the Solvers; 6 Combining Solvers to Take the Best of Them; 7 Conclusion; References; GEDLIB: A C++ Library for Graph Edit Distance Computation; 1 Introduction; 2 Overall Architecture; 3 User Interface; 4 Abstract Classes for Implementing GED Algorithms; 5 Abstract Class for Implementing Edit Costs; 6 Conclusions and Future Work; References
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2.2 Mixed Integer Linear Program2.3 F3 Formulation; 3 VPLS Heuristic; 3.1 Main Features of VPLS; 3.2 VPLS for the GED Problem; 4 Computational Experiments; 5 Conclusion; References; A Database and Evaluation for Classification of RNA Molecules Using Graph Methods; 1 Introduction; 2 Related Work; 3 Database; 4 RNA Representation; 5 Classification Methods; 5.1 Sequence-Based Methods; 5.2 Weisfeiler-Lehman Optimal Assignment (WL-OA); 5.3 Shortest Path Embedding; 5.4 All Paths and Cycles Embedding(APC); 6 Results; 7 Conclusion; References
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3.3 Training the Classifier4 Experimental Evaluation; 4.1 Database Description; 4.2 Graph Matching Performance; 4.3 Runtime Analysis; 5 Conclusions; References; Cross-Evaluation of Graph-Based Keyword Spotting in Handwritten Historical Documents; 1 Introduction; 1.1 Related Work; 1.2 Contribution; 2 Graph-Based Keyword Spotting; 2.1 Image Preprocessing; 2.2 Handwriting Graphs; 2.3 Graph Matching; 2.4 Ensemble Methods; 3 Experimental Evaluation; 3.1 Experimental Setup; 3.2 Cross-Evaluation; 3.3 Ensemble Methods; 4 Conclusion and Outlook; References
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Graph Edge Entropy in Maxwell-Boltzmann Statistics for Alzheimer's Disease Analysis1 Introduction; 2 Graph Representation; 2.1 Preliminaries; 2.2 Von Neumann Edge Entropy; 3 Thermodynamic Statistics and Global Entropy Decomposition; 3.1 Thermodynamic Entropy; 3.2 Maxwell-Boltzmann Statistics; 3.3 Edge Entropy Decomposition; 4 Experiments and Evaluations; 4.1 Dataset; 4.2 Experimental Results; 5 Conclusion; References; Solving the Graph Edit Distance Problem with Variable Partitioning Local Search; 1 Introduction; 2 GED Definition and F3 Formulation; 2.1 GED Problem Definition
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Learning the Graph Edit Costs: What Do We Want to Optimise?Abstract; 1 Introduction; 2 Attributed Graphs and Graph Edit Distance; 3 Learning Methods and Objective Functions; 4 Experimental Evaluation; 5 The Conclusions; Acknowledgments; References; Sub-optimal Graph Matching by Node-to-Node Assignment Classification; 1 Introduction; 2 Definitions; 2.1 Attributed Graphs and Graph Edit Distance; 2.2 Approximating the Graph Edit Distance; 3 Learning Graph Matching; 3.1 Learning the Edit Costs and Graph Embedding; 3.2 From Edit Costs Estimation to Node Assignment Classification
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| Abstract
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This book constitutes the refereed proceedings of the 12th IAPR-TC-15 International Workshop on Graph-Based Representation in Pattern Recognition, GbRPR 2019, held in Tours, France, in June 2019. The 22 full papers included in this volume together with an invited talk were carefully reviewed and selected from 28 submissions. The papers discuss research results and applications at the intersection of pattern recognition, image analysis, and graph theory. They cover topics such as graph edit distance, graph matching, machine learning for graph problems, network and graph embedding, spectral graph problems, and parallel algorithms for graph problems.
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Computer vision, Congresses.
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Graph theory, Congresses.
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Pattern recognition systems, Congresses.
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Computer vision.
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Graph theory.
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Pattern recognition systems.
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| Dewey Classification
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006.3
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| LC Classification
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TK7882.P3
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| Added Entry
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Conte, Donatello
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Foggia, P., (Pasquale)
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Ramel, Jean-Yves
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| Parallel Title
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GbRPR 2019
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