Machine learning techniques for multimedia : case studies on organization and retrieval / [edited by] Matthieu, Padraig.
Material type: TextSeries: Publication details: New York : Springer, 2008.Edition: 1st edDescription: xvi, 288 p. : ill. ; 24 cmISBN:- 9783540751700 (hardover : alk. paper)
- 006.31 22
Item type | Current library | Call number | Copy number | Status | Date due | Barcode | |
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Books | Main library General Stacks | 006.31 / CO.M 2008 (Browse shelf(Opens below)) | 1 | Available | 007377 |
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006.31 / AL.I 2004 Introduction to machine learning / | 006.31 / CH.L 1998 Learning from data : | 006.31 / CL.P 2009 Principles and theory for data mining and machine learning / | 006.31 / CO.M 2008 Machine learning techniques for multimedia : | 006.31 / IB.A 2010 Applied genetic programming and machine learning / | 006.31 / IN. M 2010 Multiple classifier systems : | 006.31 / PE.M 2009 Machine learning and data mining in pattern recognition : |
Includes bibliographical references and index.
1 Introduction to Bayesian Methods and Decision Theory -- 2 Supervised Learning -- 3 Unsupervised Learning and Clustering -- 4 Dimension Reduction -- 5 Online Content-Based Image Retrieval Using Active Learning -- 6 Conservative Learning for Object Detectors -- 7 Machine Learning Techniques -- 8 Mental Search in Image Databases: Implicit Versus Explicit Content Query -- 9 Combining Textual and Visual Information for Semantic Labeling of Images and Videos -- 10 Machine Learning for Semi-structured Multimedia Documents: Application to Pornographic Filtering and Thematic Categorization -- 11 Classification and Clustering of Music for Novel Music Access Applications.
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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