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Multimedia Data Mining and Analytics: Disruptive Innovation

Editat de Aaron K. Baughman, Jiang Gao, Jia-Yu Pan, Valery A. Petrushin
en Limba Engleză Hardback – 10 apr 2015
This book 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. The work 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. Features: reviews how 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 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.
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Specificații

ISBN-13: 9783319149974
ISBN-10: 3319149970
Pagini: 454
Ilustrații: XIV, 454 p. 188 illus., 153 illus. in color.
Dimensiuni: 155 x 235 x 27 mm
Greutate: 0.83 kg
Ediția:2015
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland

Public țintă

Research

Descriere

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.

Cuprins

Part I: Introduction
Disruptive Innovation: Large Scale Multimedia Data Mining
Aaron K. Baughman, Jia-Yu Pan, Jiang Gao, and Valery A. Petrushin
Part II: Mobile and Social Multimedia Data Exploration
Sentiment Analysis Using Social Multimedia
Jianbo Yuan, Quanzeng You, and Jiebo Luo
Twitter as a Personalizable Information Service
Mario Cataldi, Luigi Di Caro, and Claudio Schifanella
Mining Popular Routes from Social Media
Ling-Yin Wei, Yu Zheng, and Wen-Chih Peng
Social Interactions over Location-Aware Multimedia Systems
Yi Yu, Roger Zimmermann, and Suhua Tang
In-house Multimedia Data Mining
Christel Amato, Marc Yvon, and Wilfredo Ferré
Content-based Privacy for Consumer-Produced Multimedia
Gerald Friedland, Adam Janin, Howard Lei, Jaeyoung Choi, and Robin Sommer
Part III: Biometric Multimedia Data Processing
Large-scale Biometric Multimedia Processing
Stefan van der Stockt, Aaron Baughman, and Michael Perlitz
Detection of Demographics and Identity in Spontaneous Speech and Writing
Aaron Lawson, Luciana Ferrer, Wen Wang, and John Murray
Part IV: Multimedia Data Modeling, Search and Evaluation
Evaluating Web Image Context Extraction
Sadet Alcic and Stefan Conrad
Content Based Image Search for Clothing Recommendations in E-Commerce
Haoran Wang, Zhengzhong Zhou, Changcheng Xiao, and Liqing Zhang
Video Retrieval based on Uncertain Concept Detection using Dempster-Shafer Theory
Kimiaki Shirahama, Kenji Kumabuchi, Marcin Grzegorzek, and Kuniaki Uehara
Multimodal Fusion: Combining Visual and Textual Cues for Concept Detection in Video
Damianos Galanopoulos, Milan Dojchinovski, Krishna Chandramouli, Tomáš Kliegr, and Vasileios Mezaris
Mining Videos for Features that Drive Attention
Farhan Baluch and Laurent Itti
Exposing Image Tampering with the Same Quantization Matrix
Qingzhong Liu, Andrew H. Sung, Zhongxue Chen, and Lei Chen
Part V: Algorithms for Multimedia Data Presentation, Processing and Visualization
Fast Binary Embedding for High-Dimensional Data
Felix X. Yu, Yunchao Gong, and Sanjiv Kumar
Fast Approximate K-Means via Cluster Closures
Jingdong Wang, Jing Wang, Qifa Ke, Gang Zeng, and Shipeng Li
Fast Neighborhood Graph Search using Cartesian Concatenation
Jingdong Wang, Jing Wang, Gang Zeng, Rui Gan, Shipeng Li, and Baining Guo
Listen to the Sound of Data
Mark Last and Anna Usyskin (Gorelik)

Recenzii

“Multimedia data mining and analytics: disruptive innovation highlights new applications in multimedia data mining, presenting fascinating techniques together with comprehensive cases in practice. … this book is valuable for the insight it provides related to the challenges faced by fast developing technologies, their current needs and future promise. It is a practical guide, a useful handbook for academies and industry practitioners who have interest in multimedia data analysis.” (Shanshan Qi, Information Technology & Tourism, Vol. 16, 2016)

Notă biografică

Aaron K. Baughman is a member of the Special Events Group at IBM (USA) for World Wide Sports. Previously, he was Technical Lead on a DeepQA Embed Research project that included an instance of the Jeopardy! Challenge.
Jiang (John) Gao is a Principal Scientist in the Advanced Development and Technology Group at Nokia USA, working on multimedia and mobile applications, data mining and computer vision.
Jia-Yu Pan is a software engineer at Google (USA), working on data mining and anomaly detection in big data.
Valery A. Petrushin is a Principal Scientist in the Research and Development Group at Opera Solutions (USA). His previous publications include the successful Springer title Multimedia Data Mining and Knowledge Discovery.

Textul de pe ultima copertă

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.

Caracteristici

Presents cutting-edge multimedia data mining research, including mobile multimedia
Provides novel insights into the progression of the field, following the theme of disruptive innovation
Bridges complex research and practice by exploring open source software, libraries and algorithms
Includes supplementary material: sn.pub/extras