Structural, Syntactic, and Statistical Pattern Recognition

1. Front Matter

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2. Invited Talks

   1. From Region Based Image Representation to Object Discovery and Recognition

      • Narendra Ahuja, Sinisa Todorovic

      Pages 1-19

   2. Learning on Manifolds

      • Fatih Porikli

      Pages 20-39

   3. Classification and Trees

      • Luc Devroye

      Pages 40-44

   4. Structural Patterns in Complex Networks through Spectral Analysis

      • Ernesto Estrada

      Pages 45-59

3. Structural Descriptions

   1. Graph Embedding Using an Edge-Based Wave Kernel

      • Hewayda ElGhawalby, Edwin R. Hancock

      Pages 60-69

   2. A Structured Learning Approach to Attributed Graph Embedding

      • Haifeng Zhao, Jun Zhou, Antonio Robles-Kelly

      Pages 70-79

4. Machine Learning

   1. Combining Elimination Rules in Tree-Based Nearest Neighbor Search Algorithms

      • Eva Gómez-Ballester, Luisa Micó, Franck Thollard, Jose Oncina, Francisco Moreno-Seco

      Pages 80-89

   2. Localized Projection Learning

      • Kazuki Tsuji, Mineichi Kudo, Akira Tanaka

      Pages 90-99

   3. Entropy-Based Variational Scheme for Fast Bayes Learning of Gaussian Mixtures

      • Antonio Peñalver, Francisco Escolano, Boyan Bonev

      Pages 100-108

5. Structural Learning

   1. Learning Graph Quantization

      • Brijnesh J. Jain, S. Deepak Srinivasan, Alexander Tissen, Klaus Obermayer

      Pages 109-118

   2. High-Dimensional Spectral Feature Selection for 3D Object Recognition Based on Reeb Graphs

      • Boyan Bonev, Francisco Escolano, Daniela Giorgi, Silvia Biasotti

      Pages 119-128

   3. Dissimilarity-Based Multiple Instance Learning

      • Lauge Sørensen, Marco Loog, David M. J. Tax, Wan-Jui Lee, Marleen de Bruijne, Robert P. W. Duin

      Pages 129-138

   4. A Game Theoretic Approach to Learning Shape Categories and Contextual Similarities

      • Aykut Erdem, Andrea Torsello

      Pages 139-148

6. Poster Session

   1. A Comparison between Two Representatives of a Set of Graphs: Median vs. Barycenter Graph

      • Itziar Bardaji, Miquel Ferrer, Alberto Sanfeliu

      Pages 149-158

   2. Impact of Visual Information on Text and Content Based Image Retrieval

      • Christophe Moulin, Christine Largeron, Mathias Géry

      Pages 159-169

   3. Automatic Traffic Monitoring from Satellite Images Using Artificial Immune System

      • Mehrad Eslami, Karim Faez

      Pages 170-179

   4. Graduated Assignment Algorithm for Finding the Common Labelling of a Set of Graphs

      • Albert Solé-Ribalta, Francesc Serratosa

      Pages 180-190

   5. Affinity Propagation for Class Exemplar Mining

      • Shengping Xia, Rui Song, Edwin R. Hancock

      Pages 191-201

   6. Guided Informative Image Partitioning

      • Nathan Brewer, Nianjun Liu, Lei Wang

      Pages 202-212

This volume in the Springer Lecture Notes in Computer Science (LNCS) series contains the papers presented at the S+SSPR 2010 Workshops, which was the seventh occasion that SPR and SSPR workshops have been held jointly. S+SSPR 2010 was organized by TC1 and TC2, Technical Committees of the International Association for Pattern Recognition(IAPR), andheld inCesme, Izmir, whichis a seaside resort on the Aegean coast of Turkey. The conference took place during August 18–20, 2010, only a few days before the 20th International Conference on Pattern Recognition (ICPR) which was held in Istanbul. The aim of the series of workshops is to create an international forum for the presentation of the latest results and exchange of ideas between researchers in the ?elds of statistical and structural pattern recognition. SPR 2010 and SSPR 2010 received a total of 99 paper submissions from many di?erent countries around the world, giving it a truly international perspective, as has been the case for previous S+SSPR workshops. This volume contains 70 accepted papers, 39 for oral and 31 for poster presentation. In addition to par- lel oral sessions for SPR and SSPR, there were two joint oral sessions of interest to both SPR and SSPR communities. Furthermore, to enhance the workshop experience, there were two joint panel sessions on “Structural Learning” and “Clustering,” in which short author presentations were followed by discussion. Another innovation this year was the ?lming of the proceedings by Videol- tures.

• Hidden Markov Model
• Kernel
• algorithms
• classification
• clustering
• complexity
• data mining
• information retrieval
• learning
• machine learning
• optical character recognition (OCR)
• optimization
• pattern recognition
• search algorithm
• statistics
• algorithm analysis and problem complexity

• Computer Vision and Pattern Recognition Group,Computer Science, University of York Heslington, York, United Kingdom

  Edwin R. Hancock

• Department of Computer Science, University of York, UK

  Richard C. Wilson

• Centre for Vision, Speech and Signal Proc (CVSSP), University of Surrey, Surrey, United Kingdom

  Terry Windeatt

• Electrical and Electronics Engineering Department, Middle East Technical University, Ankara, Turkey

  Ilkay Ulusoy

• Department of Computer Science and Artificial Intelligence, University of Alicante, Alicante, Spain

  Francisco Escolano

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