Structural, Syntactic, and Statistical Pattern Recognition

Joint IAPR International Workshop, S+SSPR 2016, Mérida, Mexico, November 29 - December 2, 2016, Proceedings

  • Antonio Robles-Kelly
  • Marco Loog
  • Battista Biggio
  • Francisco Escolano
  • Richard Wilson

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)

Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 10029)

Table of contents

  1. Structural Matching

    1. Luca Rossi, Simone Severini, Andrea Torsello
      Pages 474-484
    2. Julien Lerouge, Zeina Abu-Aisheh, Romain Raveaux, Pierre Héroux, Sébastien Adam
      Pages 485-495
    3. Benoît Gaüzère, Sébastien Bougleux, Luc Brun
      Pages 496-506
    4. Carlos Francisco Moreno-García, Francesc Serratosa, Xavier Cortés
      Pages 507-518
    5. Carlos Francisco Moreno-García, Xavier Cortés, Francesc Serratosa
      Pages 519-529
    6. Francesc Serratosa, Xavier Cortés, Carlos-Francisco Moreno
      Pages 530-540
  2. Text and Document Analysis

    1. Front Matter
      Pages 541-541
    2. J. Ignacio Toledo, Sebastian Sudholt, Alicia Fornés, Jordi Cucurull, Gernot A. Fink, Josep Lladós
      Pages 543-552
    3. Michael Stauffer, Andreas Fischer, Kaspar Riesen
      Pages 553-563
    4. Michael Stauffer, Andreas Fischer, Kaspar Riesen
      Pages 564-573
    5. Sounak Dey, Anguelos Nicolaou, Josep Llados, Umapada Pal
      Pages 574-583
  3. Back Matter
    Pages 585-588

About these proceedings


This book constitutes the proceedings of the Joint IAPR International Workshop on Structural Syntactic, and Statistical Pattern Recognition, S+SSPR 2016, consisting of the International Workshop on Structural and Syntactic Pattern Recognition SSPR, and the International Workshop on Statistical Techniques in Pattern Recognition, SPR. The 51 full papers presented were carefully reviewed and selected from 68 submissions. They are organized in the following topical sections: dimensionality reduction, manifold learning and embedding methods; dissimilarity representations; graph-theoretic methods; model selection, classification and clustering; semi and fully supervised learning methods; shape analysis; spatio-temporal pattern recognition; structural matching; text and document analysis. 


complex networks machine learning optimization semantic segmentation visualization artificial intelligence biometrics database query processing and optimization graph mining graph theory and discrete mathematics image classification infromation storage and retrieval information systems multi-label classification nonlinear embedding object tracking probabilistic inference problems programming techniques semi-supervised learning structural SV

Editors and affiliations

  • Antonio Robles-Kelly
    • 1
  • Marco Loog
    • 2
  • Battista Biggio
    • 3
  • Francisco Escolano
    • 4
  • Richard Wilson
    • 5
  1. 1.Data 61 - CSIRO CanberraAustralia
  2. 2.Pattern Recognition LaboratoryTechnical University of Delft Pattern Recognition LaboratoryCD DelftThe Netherlands
  3. 3.Electrical and Electronic EngineeringUniversity of Cagliari Electrical and Electronic EngineeringCagliariItaly
  4. 4.Computación e IAUniversidad de Alicante Computación e IAAlicanteSpain
  5. 5.Computer ScienceUniversity of York Computer ScienceYorkUnited Kingdom

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-49054-0
  • Online ISBN 978-3-319-49055-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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