Machine learning techniques for multimedia : case studies on organization and retrieval
Author(s)
Bibliographic Information
Machine learning techniques for multimedia : case studies on organization and retrieval
（Cognitive technologies）
Springer, c2008
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
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. Arising from the EU MUSCLE network, this multidisciplinary book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain.
Table of Contents
to Learning Principles for Multimedia Data.- to Bayesian Methods and Decision Theory.- Supervised Learning.- Unsupervised Learning and Clustering.- Dimension Reduction.- Multimedia Applications.- Online Content-Based Image Retrieval Using Active Learning.- Conservative Learning for Object Detectors.- Machine Learning Techniques for Face Analysis.- Mental Search in Image Databases: Implicit Versus Explicit Content Query.- Combining Textual and Visual Information for Semantic Labeling of Images and Videos.- Machine Learning for Semi-structured Multimedia Documents: Application to Pornographic Filtering and Thematic Categorization.- Classification and Clustering of Music for Novel Music Access Applications.
by "Nielsen BookData"