Foundations of Large-Scale Multimedia Information Management and Retrieval
Mathematics of Perception
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Beschreibung
"Foundations of Large-Scale Multimedia Information Management and Retrieval: Mathematics of Perception" covers knowledge representation and semantic analysis of multimedia data and scalability in signal extraction, data mining, and indexing. The book is divided into two parts: Part I - Knowledge Representation and Semantic Analysis focuses on the key components of mathematics of perception as it applies to data management and retrieval. These include feature selection/reduction, knowledge representation, semantic analysis, distance function formulation for measuring similarity, and multimodal fusion. Part II - Scalability Issues presents indexing and distributed methods for scaling up these components for high-dimensional data and Web-scale datasets. The book presents some real-world applications and remarks on future research and development directions.
The book is designed for researchers, graduate students, and practitioners in the fields of Computer Vision, MachineLearning, Large-scale Data Mining, Database, and Multimedia Information Retrieval.
Dr. Edward Y. Chang was a professor at the Department of Electrical & Computer Engineering, University of California at Santa Barbara, before he joined Google as a research director in 2006. Dr. Chang received his M.S. degree in Computer Science and Ph.D degree in Electrical Engineering, both from Stanford University.
"Foundations of Large-Scale Multimedia Information Management and Retrieval: Mathematics of Perception" covers knowledge representation and semantic analysis of multimedia data and scalability in signal extraction, data mining, and indexing. The book is divided into two parts: Part I - Knowledge Representation and Semantic Analysis focuses on the key components of mathematics of perception as it applies to data management and retrieval. These include feature selection/reduction, knowledge representation, semantic analysis, distance function formulation for measuring similarity, and multimodal fusion. Part II - Scalability Issues presents indexing and distributed methods for scaling up these components for high-dimensional data and Web-scale datasets. The book presents some real-world applications and remarks on future research and development directions.
The book is designed for researchers, graduate students, and practitioners in the fields of Computer Vision, MachineLearning, Large-scale Data Mining, Database, and Multimedia Information Retrieval.
Dr. Edward Y. Chang was a professor at the Department of Electrical & Computer Engineering, University of California at Santa Barbara, before he joined Google as a research director in 2006. Dr. Chang received his M.S. degree in Computer Science and Ph.D degree in Electrical Engineering, both from Stanford University.
Written by the utmost expert at Google Inc Dr Edward Chang, Director of Research Covering knowledge representation, semantic analysis, and scalability issues in one comprehensive book Written for the specialist while maintaining an accessible approach for students alike A must-have in this ever-faster evolving area
Autor*in
Edward Y. Chang
Themen in »Foundations of Large-Scale Multimedia Information Management and Retrieval«
High-dimensional Indexing Image Retrieval Knowledge representation Large-scale Data Mining Multimedia Information Retrieval Scalability issues Semantic analysis TUP