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Table of contents

  1. Front Matter
    Pages i-xvi
  2. Francesco Camastra, Alessandro Vinciarelli
    Pages 1-10
  3. From Perception to Computation

    1. Front Matter
      Pages 11-11
    2. Francesco Camastra, Alessandro Vinciarelli
      Pages 13-55
    3. Francesco Camastra, Alessandro Vinciarelli
      Pages 57-96
  4. Machine Learning

    1. Front Matter
      Pages 97-97
    2. Francesco Camastra, Alessandro Vinciarelli
      Pages 99-106
    3. Francesco Camastra, Alessandro Vinciarelli
      Pages 107-129
    4. Francesco Camastra, Alessandro Vinciarelli
      Pages 131-167
    5. Francesco Camastra, Alessandro Vinciarelli
      Pages 169-190
    6. Francesco Camastra, Alessandro Vinciarelli
      Pages 191-227
    7. Francesco Camastra, Alessandro Vinciarelli
      Pages 229-293
    8. Francesco Camastra, Alessandro Vinciarelli
      Pages 295-340
    9. Francesco Camastra, Alessandro Vinciarelli
      Pages 341-386
  5. Applications

    1. Front Matter
      Pages 387-387
    2. Francesco Camastra, Alessandro Vinciarelli
      Pages 389-419
    3. Francesco Camastra, Alessandro Vinciarelli
      Pages 421-448
    4. Francesco Camastra, Alessandro Vinciarelli
      Pages 449-465
    5. Francesco Camastra, Alessandro Vinciarelli
      Pages 467-484
    6. Francesco Camastra, Alessandro Vinciarelli
      Pages 485-498
  6. Back Matter
    Pages 499-561

About this book

Introduction

This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book.

Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data.

Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.

Keywords

Cluster Analysis Image and Video Data Machine Learning Sequence Analysis Signal Processing

Authors and affiliations

  • Francesco Camastra
    • 1
  • Alessandro Vinciarelli
    • 2
  1. 1.University of Naples Parthenope, Department of Science and TechnologyNaplesItaly
  2. 2.University of Glasgow, School of Computing ScienceGlasgow, ScotlandUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-6735-8
  • Copyright Information Springer-Verlag London 2015
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-4471-6734-1
  • Online ISBN 978-1-4471-6735-8
  • Series Print ISSN 1610-3947
  • Series Online ISSN 2197-8441
  • Buy this book on publisher's site