Machine Learning Techniques for Multimedia | Case Studies on Organization and Retrieval

Matthieu Cord (u. a.)

ISBN 10: 3642443621 / ISBN 13: 9783642443626

Published by Springer, 2014

Machine Learning Techniques for Multimedia | Case Studies on Organization and Retrieval | Matthieu Cord (u. a.) | Taschenbuch | Previously published in hardcover | xvi | Englisch | 2014 | Springer | EAN 9783642443626 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Seller Inventory # 112047727

Synopsis:

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.

From the Back Cover:

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.

Bibliographic Details

Title: Machine Learning Techniques for Multimedia |...
Publisher: Springer
Publication Date: 2014
Binding: Taschenbuch
Condition: Neu