𝔖 Scriptorium
✦   LIBER   ✦
πŸ“

Multimedia Data Mining and Analytics: Disruptive Innovation

✍ Scribed by Aaron K. Baughman, Jiang Gao, Jia-Yu Pan, Valery A. Petrushin (eds.)

Publisher
Springer International Publishing
Year
2015
Tongue
English
Leaves
452
Edition
1
Category
Library
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✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xiv
Front Matter....Pages 1-1
Disruptive Innovation: Large Scale Multimedia Data Mining....Pages 3-28
Front Matter....Pages 29-29
Sentiment Analysis Using Social Multimedia....Pages 31-59
Twitter as a Personalizable Information Service....Pages 61-91
Mining Popular Routes from Social Media....Pages 93-116
Social Interactions over Location-Aware Multimedia Systems....Pages 117-146
In-house Multimedia Data Mining....Pages 147-155
Content-Based Privacy for Consumer-Produced Multimedia....Pages 157-173
Front Matter....Pages 175-175
Large-Scale Biometric Multimedia Processing....Pages 177-204
Detection of Demographics and Identity in Spontaneous Speech and Writing....Pages 205-225
Front Matter....Pages 227-227
Evaluating Web Image Context Extraction....Pages 229-252
Content Based Image Search for Clothing Recommendations in E-Commerce....Pages 253-267
Video Retrieval Based on Uncertain Concept Detection Using Dempster–Shafer Theory....Pages 269-294
Multimodal Fusion: Combining Visual and Textual Cues for Concept Detection in Video....Pages 295-310
Mining Videos for Features that Drive Attention....Pages 311-326
Exposing Image Tampering with the Same Quantization Matrix....Pages 327-343
Front Matter....Pages 345-345
Fast Binary Embedding for High-Dimensional Data....Pages 347-371
Fast Approximate $$K$$ K -Means via Cluster Closures....Pages 373-395
Fast Neighborhood Graph Search Using Cartesian Concatenation....Pages 397-417
Listen to the Sound of Data....Pages 419-446
Back Matter....Pages 447-454

✦ Subjects

Multimedia Information Systems; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Signal, Image and Speech Processing; Innovation/Technology Management; Artificial Intelligence (incl. Robotics)

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