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Graph-Based Representations in Pattern Recognition

5th IAPR International Workshop, GbRPR 2005, Poitiers, France, April 11-13, 2005, Proceedings

  • Conference proceedings
  • © 2005

Overview

Editors:
  1. Luc Brun

    1. GREYC CNRS UMR 6072, Image Team, Université de Caen Basse-Normandie, Caen Cedex, France

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  2. Mario Vento

    1. Dipartimento di Ingegneria dell’Informazione ed Ingegneria Elettrica, Università di Salerno, Fisciano, Italy

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3434)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: GbRPR 2005.

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Table of contents (36 papers)

  1. Hierarchical Graph Abstraction and Matching

    1. Adaptive Pyramid and Semantic Graph: Knowledge Driven Segmentation

      • Aline Deruyver, Yann Hodé, Eric Leammer, Jean-Michel Jolion
      Pages 213-222

    2. A Graph-Based Concept for Spatiotemporal Information in Cognitive Vision

      • Adrian Ion, Yll Haxhimusa, Walter G. Kropatsch
      Pages 223-232

  2. Inexact Graph Matching

    1. Approximating the Problem, not the Solution: An Alternative View of Point Set Matching

      • Tibério S. Caetano, Terry Caelli
      Pages 233-242

    2. Defining Consistency to Detect Change Using Inexact Graph Matching

      • Sidharta Gautama, Werner Goeman, Johan D’Haeyer
      Pages 243-252

    3. Asymmetric Inexact Matching of Spatially-Attributed Graphs

      • Yang Li, Dorothea Blostein, Purang Abolmaesumi
      Pages 253-262

    4. From Exact to Approximate Maximum Common Subgraph

      • Simone Marini, Michela Spagnuolo, Bianca Falcidieno
      Pages 263-272

  3. Learning

    1. Automatic Learning of Structural Models of Cartographic Objects

      • Güray Erus, Nicolas Loménie
      Pages 273-280

    2. An Experimental Comparison of Fingerprint Classification Methods Using Graphs

      • Alessandra Serrau, Gian Luca Marcialis, Horst Bunke, Fabio Roli
      Pages 281-290

    3. Collaboration Between Statistical and Structural Approaches for Old Handwritten Characters Recognition

      • Denis Arrivault, Noël Richard, Christine Fernandez-Maloigne, Philippe Bouyer
      Pages 291-300

  4. Graph Sequences

    1. Decision Trees for Error-Tolerant Graph Database Filtering

      • Christophe Irniger, Horst Bunke
      Pages 301-311

    2. Recovery of Missing Information in Graph Sequences

      • Horst Bunke, Peter Dickinson, Miro Kraetzl
      Pages 312-321

    3. Tree-Based Tracking of Temporal Image

      • Tomoya Sakai, Atsushi Imiya, Heitoh Zen
      Pages 322-331

  5. Graph Kernels

    1. Protein Classification with Kernelized Softassign

      • Miguel Angel Lozano, Francisco Escolano
      Pages 332-341

    2. Local Entropic Graphs for Globally-Consistent Graph Matching

      • Miguel Angel Lozano, Francisco Escolano
      Pages 342-351

    3. Edit Distance Based Kernel Functions for Attributed Graph Matching

      • Michel Neuhaus, Horst Bunke
      Pages 352-361

  6. Graphs and Heat Kernels

    1. A Robust Graph Partition Method from the Path-Weighted Adjacency Matrix

      • Huaijun Qiu, Edwin R. Hancock
      Pages 362-372

    2. Recent Results on Heat Kernel Embedding of Graphs

      • Xiao Bai, Edwin R. Hancock
      Pages 373-382

  7. Back Matter

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Keywords

About this book

Many vision problems have to deal with di?erent entities (regions, lines, line junctions, etc.) and their relationships. These entities together with their re- tionships may be encoded using graphs or hypergraphs. The structural inf- mation encoded by graphs allows computer vision algorithms to address both the features of the di?erent entities and the structural or topological relati- ships between them. Moreover, turning a computer vision problem into a graph problem allows one to access the full arsenal of graph algorithms developed in computer science. The Technical Committee (TC15, http://www.iapr.org/tcs.html) of the IAPR (International Association for Pattern Recognition) has been funded in order to federate and to encourage research work in these ?elds. Among its - tivities, TC15 encourages the organization of special graph sessions at many computer vision conferences and organizes the biennial workshop GbR. While being designed within a speci?c framework, the graph algorithms developed for computer vision and pattern recognition tasks often share constraints and goals with those developed in other research ?elds such as data mining, robotics and discrete geometry. The TC15 community is thus not closed in its research ?elds but on the contrary is open to interchanges with other groups/communities.

Editors and Affiliations

  • GREYC CNRS UMR 6072, Image Team, Université de Caen Basse-Normandie, Caen Cedex, France

    Luc Brun

  • Dipartimento di Ingegneria dell’Informazione ed Ingegneria Elettrica, Università di Salerno, Fisciano, Italy

    Mario Vento

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