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. Front Matter
    Pages I-XIII
  2. Dimensionality Reduction, Manifold Learning and Embedding Methods

    1. Front Matter
      Pages 1-1
    2. Lixin Cui, Lu Bai, Yue Wang, Xiao Bai, Zhihong Zhang, Edwin R. Hancock
      Pages 3-14
    3. Hayato Itoh, Atsushi Imiya, Tomoya Sakai
      Pages 37-48
    4. Giorgia Minello, Andrea Torsello, Edwin R. Hancock
      Pages 49-59
  3. Dissimilarity Representations

    1. Front Matter
      Pages 61-61
    2. Bahram Lavi, Giorgio Fumera, Fabio Roli
      Pages 63-73
    3. David M. J. Tax, Veronika Cheplygina, Robert P. W. Duin, Jan van de Poll
      Pages 84-94
    4. Mara Chinea-Rios, Germán Sanchis-Trilles, Francisco Casacuberta
      Pages 95-106
  4. Graph-Theoretic Methods

    1. Front Matter
      Pages 119-119
    2. Xavier Cortés, Francesc Serratosa, Kaspar Riesen
      Pages 121-131
    3. Romain Deville, Elisa Fromont, Baptiste Jeudy, Christine Solnon
      Pages 132-142
    4. Joshua Lockhart, Giorgia Minello, Luca Rossi, Simone Severini, Andrea Torsello
      Pages 143-152
    5. Jianjia Wang, Richard C. Wilson, Edwin R. Hancock
      Pages 153-162
    6. Cheng Ye, Richard C. Wilson, Edwin R. Hancock
      Pages 163-173
    7. Furqan Aziz, Edwin R. Hancock, Richard C. Wilson
      Pages 174-184

About these proceedings

Introduction

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. 

Keywords

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 https://doi.org/10.1007/978-3-319-49055-7
  • 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
  • About this book