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  • © 2008

Machine Learning Techniques for Multimedia

Case Studies on Organization and Retrieval

Part of the book series: Cognitive Technologies (COGTECH)

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Table of contents (11 chapters)

  1. Front Matter

    Pages I-XVI
  2. Introduction to Learning Principles for Multimedia Data

    1. Front Matter

      Pages 1-1
    2. Introduction to Bayesian Methods and Decision Theory

      • Simon P. Wilson, Rozenn Dahyot, Pádraig Cunningham
      Pages 3-19
    3. Supervised Learning

      • Pádraig Cunningham, Matthieu Cord, Sarah Jane Delany
      Pages 21-49
    4. Unsupervised Learning and Clustering

      • Derek Greene, Pádraig Cunningham, Rudolf Mayer
      Pages 51-90
    5. Dimension Reduction

      • Pádraig Cunningham
      Pages 91-112
  3. Multimedia Applications

    1. Front Matter

      Pages 113-113
    2. Online Content-Based Image Retrieval Using Active Learning

      • Matthieu Cord, Philippe-Henri Gosselin
      Pages 115-138
    3. Conservative Learning for Object Detectors

      • Peter M. Roth, Horst Bischof
      Pages 139-158
    4. Machine Learning Techniques for Face Analysis

      • Roberto Valenti, Nicu Sebe, Theo Gevers, Ira Cohen
      Pages 159-187
    5. Mental Search in Image Databases: Implicit Versus Explicit Content Query

      • Simon P. Wilson, Julien Fauqueur, Nozha Boujemaa
      Pages 189-204
    6. Combining Textual and Visual Information for Semantic Labeling of Images and Videos

      • Pinar Duygulu, Muhammet Baştan, Derya Ozkan
      Pages 205-225
  4. Back Matter

    Pages 287-288

About this book

Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Applying machine learning techniques to multimedia content involves special considerations – the data is typically of very high dimension, and the normal distinction between supervised and unsupervised techniques does not always apply.

This book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain. Arising from the EU MUSCLE network, a program that drew together multidisciplinary teams with expertise in machine learning, pattern recognition, artificial intelligence, and image, video, text and crossmedia processing, the book first introduces the machine learning principles and techniques that are applied in multimedia data processing and analysis. The second part focuses on multimedia data processing applications, with chapters examining specific machine learning issues in domains such as image retrieval, biometrics, semantic labelling, mobile devices, and mining in text and music.

This book will be suitable for practitioners, researchers and students engaged with machine learning in multimedia applications.

Editors and Affiliations

  • UPMC University, CNRS (UMR 7606) Lab. LIP6, France

    Matthieu Cord

  • University College Dublin, School of Computer Science & Informatics, Belfield, Ireland

    Pádraig Cunningham

Bibliographic Information

Buy it now

Buying options

eBook USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access