Structural, Syntactic, and Statistical Pattern Recognition

1. Kernel Methods in Structural and Syntactical Pattern Recognition

   1. A Jensen-Shannon Kernel for Hypergraphs

      • Lu Bai, Edwin R. Hancock, Peng Ren

      Pages 181-189

   2. Heat Flow-Thermodynamic Depth Complexity in Directed Networks

      • Francisco Escolano, Boyan Bonev, Edwin R. Hancock

      Pages 190-198

   3. Shape Similarity Based on a Treelet Kernel with Edition

      • Sébastien Bougleux, François-Xavier Dupé, Luc Brun, Myriam Mokhtari

      Pages 199-207

2. Applications of Structural and Syntactical Pattern Recognition

   1. 3D Shape Classification Using Commute Time

      • Muhammad Haseeb, Edwin R. Hancock

      Pages 208-215

   2. Conditional Random Fields for Land Use/Land Cover Classification and Complex Region Detection

      • Gulcan Can, Orhan Firat, Fatos Tunay Yarman Vural

      Pages 216-224

   3. Recognition of Long-Term Behaviors by Parsing Sequences of Short-Term Actions with a Stochastic Regular Grammar

      • Gerard Sanromà, Gertjan Burghouts, Klamer Schutte

      Pages 225-233

   4. A Comparison between Structural and Embedding Methods for Graph Classification

      • Albert Solé-Ribalta, Xavier Cortés, Francesc Serratosa

      Pages 234-242

   5. Improving Fuzzy Multilevel Graph Embedding through Feature Selection Technique

      • Muhammad Muzzamil Luqman, Jean Yves Ramel, Josep Lladós

      Pages 243-253

3. Clustering

   1. Dynamic Learning of SCRF for Feature Selection and Classification of Hyperspectral Imagery

      • Ping Zhong, Zhiming Qian, Runsheng Wang

      Pages 254-263

   2. Entropic Selection of Histogram Features for Efficient Classification

      • Ákos Utasi

      Pages 264-272

   3. 2D Shapes Classification Using BLAST

      • Pietro Lovato, Manuele Bicego

      Pages 273-281

   4. A New Random Forest Method for One-Class Classification

      • Chesner Désir, Simon Bernard, Caroline Petitjean, Laurent Heutte

      Pages 282-290

   5. A New Index Based on Sparsity Measures for Comparing Fuzzy Partitions

      • Romain Quéré, Carl Frélicot

      Pages 291-300

4. Learning

   1. Polichotomies on Imbalanced Domains by One-per-Class Compensated Reconstruction Rule

      • Roberto D’Ambrosio, Paolo Soda

      Pages 301-309

   2. The Dipping Phenomenon

      • Marco Loog, Robert P. W. Duin

      Pages 310-317

   3. Colour Matching Function Learning

      • Luis Romero-Ortega, Antonio Robles-Kelly

      Pages 318-326

   4. Constrained Log-Likelihood-Based Semi-supervised Linear Discriminant Analysis

      • Marco Loog, Are C. Jensen

      Pages 327-335

   5. Out-of-Sample Embedding by Sparse Representation

      • Bogdan Raducanu, Fadi Dornaika

      Pages 336-344

5. Kernel Methods in Statistical Pattern Recognition

   1. Extended Analyses for an Optimal Kernel in a Class of Kernels with an Invariant Metric

      • Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo

      Pages 345-353

   2. Simultaneous Learning of Localized Multiple Kernels and Classifier with Weighted Regularization

      • Naoya Inoue, Yukihiko Yamashita

      Pages 354-362

This volume constitutes the refereed proceedings of the Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2012) and Statistical Techniques in Pattern Recognition (SPR 2012), held in Hiroshima, Japan, in November 2012 as a satellite event of the 21st International Conference on Pattern Recognition, ICPR 2012. The 80 revised full papers presented together with 1 invited paper and the Pierre Devijver award lecture were carefully reviewed and selected from more than 120 initial submissions. The papers are organized in topical sections on structural, syntactical, and statistical pattern recognition, graph and tree methods, randomized methods and image analysis, kernel methods in structural and syntactical pattern recognition, applications of structural and syntactical pattern recognition, clustering, learning, kernel methods in statistical pattern recognition, kernel methods in statistical pattern recognition, as well as applications of structural, syntactical, and statistical methods.

• collective classification
• genetic optimization
• kernel methods
• neural networks
• semantic parts
• algorithm analysis and problem complexity

• Department of Computer Science, University of Auckland, Auckland, New Zealand

  Georgy Gimel’farb

• Department of Computer Science, University of York, York, UK

  Edwin Hancock

• Institute of Media and Information Technology, Chiba University, Inage-ku, Japan

  Atsushi Imiya

• Technische Universität/Fraunhofer IGD, Darmstadt, Germany

  Arjan Kuijper

• Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan

  Mineichi Kudo

• Graduate School of Engineering, Tohoku University, Sendai, Japan

  Shinichiro Omachi

• Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK

  Terry Windeatt

• C&C Innovation Research Laboratories, NEC Corporation, Ikoma-Shi, Japan

  Keiji Yamada

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