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Multimedia Data Mining and Analytics = Disruptive Innovation /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Multimedia Data Mining and Analytics/ edited by Aaron K. Baughman, Jiang Gao, Jia-Yu Pan, Valery A. Petrushin.
其他題名:	Disruptive Innovation /
其他作者:	Baughman, Aaron K.
面頁冊數:	XIV, 454 p. 188 illus., 153 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Multimedia information systems. -
電子資源:	https://doi.org/10.1007/978-3-319-14998-1
ISBN:	9783319149981

Multimedia Data Mining and Analytics = Disruptive Innovation /
Multimedia Data Mining and AnalyticsDisruptive Innovation /[electronic resource] :edited by Aaron K. Baughman, Jiang Gao, Jia-Yu Pan, Valery A. Petrushin. - 1st ed. 2015. - XIV, 454 p. 188 illus., 153 illus. in color.online resource.

Part I: Introduction -- Disruptive Innovation: Large Scale Multimedia Data Mining -- Part II: Mobile and Social Multimedia Data Exploration -- Sentiment Analysis Using Social Multimedia -- Twitter as a Personalizable Information Service -- Mining Popular Routes from Social Media -- Social Interactions over Location-Aware Multimedia Systems -- In-house Multimedia Data Mining -- Content-based Privacy for Consumer-Produced Multimedia -- Part III: Biometric Multimedia Data Processing -- Large-scale Biometric Multimedia Processing -- Detection of Demographics and Identity in Spontaneous Speech and Writing -- Part IV: Multimedia Data Modeling, Search and Evaluation -- Evaluating Web Image Context Extraction -- Content Based Image Search for Clothing Recommendations in E-Commerce -- Video Retrieval based on Uncertain Concept Detection using Dempster-Shafer Theory -- Multimodal Fusion: Combining Visual and Textual Cues for Concept Detection in Video -- Mining Videos for Features that Drive Attention -- Exposing Image Tampering with the Same Quantization Matrix -- Part V: Algorithms for Multimedia Data Presentation, Processing and Visualization -- Fast Binary Embedding for High-Dimensional Data -- Fast Approximate K-Means via Cluster Closures -- Fast Neighborhood Graph Search using Cartesian Concatenation -- Listen to the Sound of Data.

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.

ISBN: 9783319149981

Standard No.: 10.1007/978-3-319-14998-1doiSubjects--Topical Terms:

1115395
Multimedia information systems.

LC Class. No.: QA76.575

Dewey Class. No.: 006.7

Multimedia Data Mining and Analytics = Disruptive Innovation /
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