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EBOOK
Title Multimedia Data Mining and Analytics : Disruptive Innovation / edited by Aaron K. Baughman, Jiang Gao, Jia-Yu Pan, Valery A. Petrushin.
Imprint Cham : Springer International Publishing, 2015.

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Subject Computer science.
Data mining.
Multimedia systems.
Artificial intelligence.
Computer vision.
Alt Name Baughman, Aaron K.
Gao, Jiang.
Pan, Jia-Yu.
Petrushin, V. A. (Valeriĭ Aleksandrovich)
Ohio Library and Information Network.
Description Online-Ressource
Summary This authoritative text/reference provides fresh insights into the cutting edge of multimedia data mining, reflecting how the research focus has shifted towards networked social communities, mobile devices and sensors. Presenting a detailed exploration into the progression of the field, the book describes how the history of multimedia data processing can be viewed as a sequence of disruptive innovations. Across the chapters, the discussion covers the practical frameworks, libraries, and open source software that enable the development of ground-breaking research into practical applications. Topics and features: · Contains contributions from an international selection of pre-eminent authorities in the field · Reviews how disruptive innovations in mobile, social, cognitive, cloud and organic based computing impacts upon the development of multimedia data mining · Provides practical details on implementing the technology for solving real-world multimedia problems · Includes chapters devoted to privacy issues in multimedia social environments, and large-scale biometric data processing · Covers content and concept based multimedia search, and advanced algorithms for multimedia data representation, processing and visualization The illuminating viewpoints presented in this comprehensive volume will be of great interest to researchers and graduate students involved in machine learning and pattern recognition, as well as to professional multimedia analysts and software developers.
Access Available to OhioLINK libraries.
ISBN 9783319149974 (print)
3319149970 (print)
9783319149981 (eISBN)
3319149989 (eISBN)
ISBN/ISSN 10.1007/978-3-319-14998-1
OCLC # 907012236