Machine Learning Techniques for Multimedia
Case Studies on Organization and Retrieval
Editors: Cord, Matthieu, Cunningham, Padraig (Eds.)
- Suitable for practitioners, researchers and students engaged with machine learning in multimedia applications
- Focus on multimedia data processing applications
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- 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.
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Bibliographic Information
- Bibliographic Information
-
- Book Title
- Machine Learning Techniques for Multimedia
- Book Subtitle
- Case Studies on Organization and Retrieval
- Editors
-
- Matthieu Cord
- Padraig Cunningham
- Series Title
- Cognitive Technologies
- Copyright
- 2008
- Publisher
- Springer-Verlag Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- eBook ISBN
- 978-3-540-75171-7
- DOI
- 10.1007/978-3-540-75171-7
- Hardcover ISBN
- 978-3-540-75170-0
- Softcover ISBN
- 978-3-642-44362-6
- Series ISSN
- 1611-2482
- Edition Number
- 1
- Number of Pages
- XVI, 289
- Topics
