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Similarity-Based Pattern Recognition

First International Workshop, SIMBAD 2011, Venice, Italy, September 28-30, 2011, Proceedings

  • Conference proceedings
  • © 2011

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Editors:
  1. Marcello Pelillo

    1. DAIS, Università Ca’ Foscari, Venice, Italy

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  2. Edwin R. Hancock

    1. The University of York, Heslington, UK

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 7005)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

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Conference proceedings info: SIMBAD 2011.

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About this book

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.

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Table of contents (23 papers)

  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
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Other volumes

  1. Similarity-Based Pattern Recognition

Editors and Affiliations

  • DAIS, Università Ca’ Foscari, Venice, Italy

    Marcello Pelillo

  • The University of York, Heslington, UK

    Edwin R. Hancock

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