Foundations of LargeScale Multimedia Information Management and Retrieval: Mathematics of Perception,Used

Product Description "Foundations of LargeScale 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 highdimensional data and Webscale datasets. The book presents some realworld 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, Machine Learning, Largescale 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. From the Back Cover "Foundations of LargeScale 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 highdimensional data and Webscale datasets. The book presents some realworld 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, Machine Learning, Largescale 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. About the Author 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. Edward Y. Chang received his M.S. degree in Computer Science and Ph.D degree in Electrical Engineering, both from Stanford University.