Graph-Based Representations in Pattern Recognition - Info and Reading Options

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Intro -- Preface -- Organization -- Invited Talks -- Approaches to Analysis of Large Networks -- Graph Edit Distance: Basics and History -- Contents -- Image and Shape Analysis -- Saliency Detection via A Graph Based Diffusion Model -- 1 Introduction -- 2 Brief Review of Random Walk with Restart -- 3 Saliency Detection -- 3.1 Graph Construction -- 3.2 Diffusion with Background Prior -- 3.3 Diffusion with Foreground Prior -- 3.4 Combination -- 4 Experiments -- 4.1 Datasets and Settings -- 4.2 Results -- 5 Conclusions -- References -- Shape Simplification Through Graph Sparsification -- 1 Introduction -- 1.1 Shape Representations: Triangulations vs Alpha Shapes -- 1.2 Contributions -- 2 Graph Sparsification -- 2.1 Definition and Ingredients -- 2.2 Spectral Formulation and Effective Resistances -- 3 Experiments -- 4 Conclusions -- References -- Reeb Graphs of Piecewise Linear Functions -- 1 Introduction -- 2 The Topological Reeb Graph and Its Properties -- 3 Preliminary Facts on Polyhedra -- 4 The Reeb Graph is a Graph Also in the PL Case -- 5 Conclusions -- References -- Learning and Graph Kernels -- Learning from Diffusion-Weighted Magnetic Resonance Images Using Graph Kernels -- 1 Introduction -- 2 Methods -- 2.1 Constructing DWI-Based Parcellations -- 2.2 Graph Construction -- 2.3 Learning from Graphs -- 3 Experiments and Results -- 3.1 Aging Trajectory of the Corpus Callosum -- 3.2 Data and Experiments -- 3.3 Results -- 4 Discussion and Future Work -- References -- Learning Graph Matching with a Graph-Based Perceptron in a Classification Context -- 1 Introduction -- 2 Problem Statement -- 3 State of the Art -- 4 Proposal: A Graph-Based Perceptron -- 5 Experiments -- 6 Conclusion -- References -- A Nested Alignment Graph Kernel Through the Dynamic Time Warping Framework -- 1 Introduction -- 2 Preliminary Concepts

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