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Multimedia Data Mining and Analytics

Disruptive Innovation

  • Book
  • © 2015

Overview

Editors:
  1. Aaron K. Baughman

    1. IBM Corp., Durham, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Jiang Gao

    1. Nokia Inc., Sunnyvale, USA

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  3. Jia-Yu Pan

    1. Google Inc., Mountain View, USA

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  4. Valery A. Petrushin

    1. 4i, Inc., Carlsbad, USA

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  • 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

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Table of contents (19 chapters)

  1. Front Matter

    Pages i-xiv
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  2. Introduction

    1. Front Matter

      Pages 1-1
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    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
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    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
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    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
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    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
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Keywords

About this book

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.

Reviews

“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)

Editors and Affiliations

  • 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

About the editors

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.

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