Multimedia Data Mining and Analytics

Disruptive Innovation
Softcover Nachdruck of the original 1. Auflage 2015
ISBN: 978-3-319-34721-9
Verlag: Springer International Publishing

Buch, Englisch, 454 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 7874 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Baughman, Aaron K.

Petrushin, Valery A.

Gao, Jiang

Pan, Jia-Yu

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
• Mathematik | Informatik EDV | Informatik Informatik Tonsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Multimedia

Inhaltsverzeichnis

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 Ferre´

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, Toma´š 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)

Über Autor(innen)

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.