Multimedia Systems and Applications

Machine Learning for Multimedia Content Analysis

Authors: Gong, Yihong, Xu, Wei

  • Details unique problems and interesting applications of machine learning in multimedia
  • Includes examples of unsupervised learning, generative models and discriminative models
  • Includes Maximum Margin Markov (M3) networks, which strive to combine the advantages of both the graphical models and Support Vector Machines (SVM)
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eBook $119.00
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  • ISBN 978-0-387-69942-4
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Hardcover $159.00
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  • ISBN 978-0-387-69938-7
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Softcover $159.00
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  • ISBN 978-1-4419-4353-8
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About this book

Challenges in complexity and variability of multimedia data have led to revolutions in machine learning techniques. Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, isolated data items. A number of pixels in a digital image collectively conveys certain visual content to viewers. A TV video program consists of both audio and image streams that unfold the underlying story.  To recognize the visual content of a digital image, or to understand the underlying story of a video program, we may need to label sets of pixels or groups of image and audio frames jointly.

Machine Learning for Multimedia Content Analysis introduces machine learning techniques that are particularly powerful and effective for modeling spatial, temporal structures of multimedia data and for accomplishing common tasks of multimedia content analysis. This book systematically covers these techniques in an intuitive fashion and demonstrates their applications through case studies. This volume uses a large number of figures to illustrate and visualize complex concepts, and provides insights into the characteristics of many algorithms through examinations of their loss functions and straightforward comparisons.

Machine Learning for Multimedia Content Analysis is designed for an academic and professional audience. Researchers will find this book an invaluable tool for applying machine learning techniques to multimedia content analysis. This volume is also suitable for practitioners in industry.

 

Reviews

From the reviews:

"The objectives of this book are to bring together powerful machine learning techniques that are suitable for modeling multimedia data, and to showcase their application to common multimedia content analysis tasks. The book is designed for students and researchers who want to apply machine learning techniques to multimedia content analysis. … Motivated researchers working in this field can certainly benefit by reading about the methods and case studies described here. It could also serve as a good reference … ." (Rao Vemuri, Computing Reviews, Vol. 50 (1), January, 2009)


Table of contents (2 chapters)

Buy this book

eBook $119.00
price for USA
  • ISBN 978-0-387-69942-4
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Download immediately after purchase
Hardcover $159.00
price for USA
  • ISBN 978-0-387-69938-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $159.00
price for USA
  • ISBN 978-1-4419-4353-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.

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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning for Multimedia Content Analysis
Authors
Series Title
Multimedia Systems and Applications
Series Volume
30
Copyright
2007
Publisher
Springer US
Copyright Holder
Springer-Verlag US
eBook ISBN
978-0-387-69942-4
DOI
10.1007/978-0-387-69942-4
Hardcover ISBN
978-0-387-69938-7
Softcover ISBN
978-1-4419-4353-8
Edition Number
1
Number of Pages
XVI, 277
Number of Illustrations and Tables
20 b/w illustrations
Topics