Similarity-Based Pattern Analysis and Recognition

  • Marcello Pelillo

Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Marcello Pelillo
    Pages 1-10
  3. Foundational Issues

    1. Front Matter
      Pages 11-11
    2. Robert P. W. Duin, Elżbieta Pękalska, Marco Loog
      Pages 13-44
    3. Joachim M. Buhmann
      Pages 45-64
  4. Deriving Similarities for Non-vectorial Data

    1. Front Matter
      Pages 65-65
    2. Pedro M. Q. Aguiar, Manuele Bicego, Umberto Castellani, Mário A. T. Figueiredo, André T. Martins, Vittorio Murino et al.
      Pages 67-83
    3. Ana L. N. Fred, André Lourenço, Helena Aidos, Samuel Rota Bulò, Nicola Rebagliati, Mário A. T. Figueiredo et al.
      Pages 85-117
  5. Embedding and Beyond

    1. Front Matter
      Pages 119-119
    2. Peng Ren, Furqan Aziz, Lin Han, Eliza Xu, Richard C. Wilson, Edwin R. Hancock
      Pages 121-155
    3. Volker Roth, Thomas J. Fuchs, Julia E. Vogt, Sandhya Prabhakaran, Joachim M. Buhmann
      Pages 157-177
    4. Marcello Pelillo, Samuel Rota Bulò, Andrea Torsello, Andrea Albarelli, Emanuele Rodolà
      Pages 179-216
  6. Applications

    1. Front Matter
      Pages 217-217
    2. Peter J. Schüffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth, Joachim M. Buhmann
      Pages 219-245
    3. Aydın Ulaş, Umberto Castellani, Manuele Bicego, Vittorio Murino, Marcella Bellani, Michele Tansella et al.
      Pages 247-287
  7. Back Matter
    Pages 289-291

About this book

Introduction

The pattern recognition and machine learning communities have, until recently, focused mainly on feature-vector representations, typically considering objects in isolation. However, this paradigm is being increasingly challenged by similarity-based approaches, which recognize the importance of relational and similarity information.

This accessible text/reference presents a coherent overview of the emerging field of non-Euclidean similarity learning. The book presents a broad range of perspectives on similarity-based pattern analysis and recognition methods, from purely theoretical challenges to practical, real-world applications. The coverage includes both supervised and unsupervised learning paradigms, as well as generative and discriminative models.

Topics and features:

  • Explores the origination and causes of non-Euclidean (dis)similarity measures, and how they influence the performance of traditional classification algorithms
  • Reviews similarity measures for non-vectorial data, considering both a “kernel tailoring” approach and a strategy for learning similarities directly from training data
  • Describes various methods for “structure-preserving” embeddings of structured data
  • Formulates classical pattern recognition problems from a purely game-theoretic perspective
  • Examines two large-scale biomedical imaging applications that provide assistance in the diagnosis of physical and mental illnesses from tissue microarray images and MRI images

This pioneering work is essential reading for graduate students and researchers seeking an introduction to this important and diverse subject.

Marcello Pelillo is a Full Professor of Computer Science at the University of Venice, Italy. He is a Fellow of the IEEE and of the IAPR.

Keywords

Computer Vision Image Analysis Machine Learning Pattern Recognition

Editors and affiliations

  • Marcello Pelillo
    • 1
  1. 1.DAISCa' Foscari University of VeniceVenezia MestreItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-5628-4
  • Copyright Information Springer-Verlag London 2013
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-4471-5627-7
  • Online ISBN 978-1-4471-5628-4
  • Series Print ISSN 2191-6586
  • Series Online ISSN 2191-6594
  • About this book