Similarity-Based Pattern Recognition

1. Front Matter

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2. Dissimilarity Characterization and Analysis

   1. On the Usefulness of Similarity Based Projection Spaces for Transfer Learning

      • Emilie Morvant, Amaury Habrard, Stéphane Ayache

      Pages 1-16

   2. Metric Anomaly Detection via Asymmetric Risk Minimization

      • Aryeh Kontorovich, Danny Hendler, Eitan Menahem

      Pages 17-30

   3. One Shot Similarity Metric Learning for Action Recognition

      • Orit Kliper-Gross, Tal Hassner, Lior Wolf

      Pages 31-45

   4. On a Non-monotonicity Effect of Similarity Measures

      • Bernhard Moser, Gernot Stübl, Jean-Luc Bouchot

      Pages 46-60

   5. Section-Wise Similarities for Clustering and Outlier Detection of Subjective Sequential Data

      • Oscar S. Siordia, Isaac Martín de Diego, Cristina Conde, Enrique Cabello

      Pages 61-76

3. Generative Models of Similarity Data

   1. Hybrid Generative-Discriminative Nucleus Classification of Renal Cell Carcinoma

      • Aydın Ulaş, Peter J. Schüffler, Manuele Bicego, Umberto Castellani, Vittorio Murino

      Pages 77-89

   2. Multi-task Regularization of Generative Similarity Models

      • Luca Cazzanti, Sergey Feldman, Maya R. Gupta, Michael Gabbay

      Pages 90-103

   3. A Generative Dyadic Aspect Model for Evidence Accumulation Clustering

      • André Lourenço, Ana Fred, Mário Figueiredo

      Pages 104-116

4. Graph Based and Relational Models

   1. Supervised Learning of Graph Structure

      • Andrea Torsello, Luca Rossi

      Pages 117-132

   2. An Information Theoretic Approach to Learning Generative Graph Prototypes

      • Lin Han, Edwin R. Hancock, Richard C. Wilson

      Pages 133-148

   3. Graph Characterization via Backtrackless Paths

      • Furqan Aziz, Richard C. Wilson, Edwin R. Hancock

      Pages 149-162

   4. Impact of the Initialization in Tree-Based Fast Similarity Search Techniques

      • Aureo Serrano, Luisa Micó, Jose Oncina

      Pages 163-176

5. Clustering and Dissimilarity Data

   1. Multiple-Instance Learning with Instance Selection via Dominant Sets

      • Aykut Erdem, Erkut Erdem

      Pages 177-191

   2. Min-sum Clustering of Protein Sequences with Limited Distance Information

      • Konstantin Voevodski, Maria-Florina Balcan, Heiko Röglin, Shang-Hua Teng, Yu Xia

      Pages 192-206

   3. Model-Based Clustering of Inhomogeneous Paired Comparison Data

      • Ludwig M. Busse, Joachim M. Buhmann

      Pages 207-221

   4. Bag Dissimilarities for Multiple Instance Learning

      • David M. J. Tax, Marco Loog, Robert P. W. Duin, Veronika Cheplygina, Wan-Jui Lee

      Pages 222-234

   5. Mutual Information Criteria for Feature Selection

      • Zhihong Zhang, Edwin R. Hancock

      Pages 235-249

6. Applications

   1. Combining Data Sources Nonlinearly for Cell Nucleus Classification of Renal Cell Carcinoma

      • Mehmet Gönen, Aydın Ulaş, Peter Schüffler, Umberto Castellani, Vittorio Murino

      Pages 250-260

   2. Supervised Segmentation of Fiber Tracts

      • Emanuele Olivetti, Paolo Avesani

      Pages 261-274

This book constitutes the proceedings of the First International Workshop on Similarity Based Pattern Recognition, SIMBAD 2011, held in Venice, Italy, in September 2011. The 16 full papers and 7 poster papers presented were carefully reviewed and selected from 35 submissions. The contributions are organized in topical sections on dissimilarity characterization and analysis; generative models of similarity data; graph-based and relational models; clustering and dissimilarity data; applications; spectral methods and embedding.

• graph clustering
• multi-task learning
• stereo matching
• support vector machines
• unsupervised learning
• algorithm analysis and problem complexity

• DAIS, Università Ca’ Foscari, Venice, Italy

  Marcello Pelillo

• The University of York, Heslington, UK

  Edwin R. Hancock

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