Machine Learning for Audio, Image and Video Analysis

Theory and Applications

By Francesco Camastra , Alessandro Vinciarelli

Machine Learning for Audio, Image and Video Analysis Cover Image

This book illustrates how to deal with complex media and convert raw data into useful information. Students and researchers needing a solid foundation or reference, and practitioners interested in discovering more about the state-of-the-art will find this book invaluable.

Full Description

  • ISBN13: 978-1-8480-0006-3
  • 512 Pages
  • User Level: Students
  • Publication Date: December 22, 2007
  • Available eBook Formats: PDF
  • eBook Price: $139.00
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Full Description
This book illustrates how to deal with complex media and convert raw data into useful information. Once the original data have been converted into a digital representation, the processing of different media can be performed under the unifying framework of machine learning. Therefore only part of the information extraction processes is media specific and most work can be made by applying general techniques valid for a wide range of problems. Apparently very different domains like face verification and text categorization become, from a processing point of view, equivalent. Organized into 3 parts: The 1st focuses on technical aspects, basic mathematical notions and elementary machine learning techniques. The 2nd provides an extensive survey of most relevant machine learning techniques for media processing. The 3rd focuses on applications and shows how techniques are applied in actual problems. This unique book offers an introduction and advanced material in the combined fields of machine learning and image/video processing.
Table of Contents

Table of Contents

  1. Introduction.
  2. Part 1: From Perception to Computation.
  3. Audio Acquisition, Representation and Storage.
  4. Image and Video Acquisition, Representation and Storage.
  5. Part 2: Machine Learning.
  6. Machine Learning.
  7. Bayesian Theory of Decision.
  8. Clustering Methods.
  9. Foundations of Statistical Learning and Model Selection.
  10. Supervised Neural Networks and Ensemble Methods.
  11. Kernel Methods.
  12. Markovian Models for Sequential Data.
  13. Feature Extraction and Manifold Learning Methods.
  14. Part 3: Applications.
  15. Speech and Handwriting Recognition.
  16. Automatic Face Recognition.
  17. Video Segmentation and Keyframe Extraction.
  18. Part 4: Appendices.
  19. Statistics.
  20. Signal Processing.
  21. Matrix Algebra.
  22. Mathematical Foundations of Kernel Methods.
  23. Index.
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