Multimedia Data Mining and Analytics

1. Front Matter

   Pages i-xiv

   PDF

2. Introduction

   1. Front Matter

      Pages 1-1

      PDF

   2. Disruptive Innovation: Large Scale Multimedia Data Mining

      • Aaron K. Baughman, Jia-Yu Pan, Jiang Gao, Valery A. Petrushin

      Pages 3-28

3. Mobile and Social Multimedia Data Exploration

   1. Front Matter

      Pages 29-29

      PDF

   2. Sentiment Analysis Using Social Multimedia

      • Jianbo Yuan, Quanzeng You, Jiebo Luo

      Pages 31-59

   3. Twitter as a Personalizable Information Service

      • Mario Cataldi, Luigi Di Caro, Claudio Schifanella

      Pages 61-91

   4. Mining Popular Routes from Social Media

      • Ling-Yin Wei, Yu Zheng, Wen-Chih Peng

      Pages 93-116

   5. Social Interactions over Location-Aware Multimedia Systems

      • Yi Yu, Roger Zimmermann, Suhua Tang

      Pages 117-146

   6. In-house Multimedia Data Mining

      • Christel Amato, Marc Yvon, Wilfredo Ferré

      Pages 147-155

   7. Content-Based Privacy for Consumer-Produced Multimedia

      • Gerald Friedland, Adam Janin, Howard Lei, Jaeyoung Choi, Robin Sommer

      Pages 157-173

4. Biometric Multimedia Data Processing

   1. Front Matter

      Pages 175-175

      PDF

   2. Large-Scale Biometric Multimedia Processing

      • Stefan van der Stockt, Aaron K. Baughman, Michael Perlitz

      Pages 177-204

   3. Detection of Demographics and Identity in Spontaneous Speech and Writing

      • Aaron Lawson, Luciana Ferrer, Wen Wang, John Murray

      Pages 205-225

5. Multimedia Data Modeling, Search and Evaluation

   1. Front Matter

      Pages 227-227

      PDF

   2. Evaluating Web Image Context Extraction

      • Sadet Alcic, Stefan Conrad

      Pages 229-252

   3. Content Based Image Search for Clothing Recommendations in E-Commerce

      • Haoran Wang, Zhengzhong Zhou, Changcheng Xiao, Liqing Zhang

      Pages 253-267

   4. Video Retrieval Based on Uncertain Concept Detection Using Dempster–Shafer Theory

      • Kimiaki Shirahama, Kenji Kumabuchi, Marcin Grzegorzek, Kuniaki Uehara

      Pages 269-294

   5. Multimodal Fusion: Combining Visual and Textual Cues for Concept Detection in Video

      • Damianos Galanopoulos, Milan Dojchinovski, Krishna Chandramouli, Tomáš Kliegr, Vasileios Mezaris

      Pages 295-310

   6. Mining Videos for Features that Drive Attention

      • Farhan Baluch, Laurent Itti

      Pages 311-326

   7. Exposing Image Tampering with the Same Quantization Matrix

      • Qingzhong Liu, Andrew H. Sung, Zhongxue Chen, Lei Chen

      Pages 327-343

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.

• Audio Recognition
• Computer Vision
• Disruptive Innovation
• Image Processing
• Machine Learning
• Multi-Modal Data Processing
• Multimedia Data Mining
• Natural Language Processing
• Signal Processing

“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)

• IBM Corp., Durham, USA

  Aaron K. Baughman

• Nokia Inc., Sunnyvale, USA

  Jiang Gao

• Google Inc., Mountain View, USA

  Jia-Yu Pan

• 4i, Inc., Carlsbad, USA

  Valery A. Petrushin

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.

Policies and ethics