© 2007

Machine Learning for Multimedia Content Analysis


Table of contents

  1. Front Matter
    Pages I-XV
  2. Unsupervised Learning

    1. Pages 1-11
    2. Pages 15-35
  3. Generative Graphical Models

  4. Discriminative Graphical Models

  5. Back Matter
    Pages 268-277

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.



DOM Dimensionsreduktion Gong Hidden Markov Model Machine Learning Maximum Margin Markov (M3) networks Multimedia Simulation Support Vector Machine Techniques Technology algorithms complexity learning networks

Authors and affiliations

  1. 1.NEC Laboratories America, Inc.CupertinoUSA

Bibliographic information

Industry Sectors
IT & Software


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)