Big Data Analytics for Large-Scale Multimedia Search
1. Auflage April 2019
376 Seiten, Hardcover
Wiley & Sons Ltd
Weitere Versionen
A timely overview of cutting edge technologies for multimedia retrieval with a special emphasis on scalability
The amount of multimedia data available every day is enormous and is growing at an exponential rate, creating a great need for new and more efficient approaches for large scale multimedia search. This book addresses that need, covering the area of multimedia retrieval and placing a special emphasis on scalability. It reports the recent works in large scale multimedia search, including research methods and applications, and is structured so that readers with basic knowledge can grasp the core message while still allowing experts and specialists to drill further down into the analytical sections.
Big Data Analytics for Large-Scale Multimedia Search covers: representation learning, concept and event-based video search in large collections; big data multimedia mining, large scale video understanding, big multimedia data fusion, large-scale social multimedia analysis, privacy and audiovisual content, data storage and management for big multimedia, large scale multimedia search, multimedia tagging using deep learning, interactive interfaces for big multimedia and medical decision support applications using large multimodal data.
* Addresses the area of multimedia retrieval and pays close attention to the issue of scalability
* Presents problem driven techniques with solutions that are demonstrated through realistic case studies and user scenarios
* Includes tables, illustrations, and figures
* Offers a Wiley-hosted BCS that features links to open source algorithms, data sets and tools
Big Data Analytics for Large-Scale Multimedia Search is an excellent book for academics, industrial researchers, and developers interested in big multimedia data search retrieval. It will also appeal to consultants in computer science problems and professionals in the multimedia industry.
Chapter 1: Introduction
Chapter 2: Feature extraction from big multimedia data
2.1. Representation Learning, Models and Practice
2.2. Concept-based and event-based search in large video collections
2.3. Big Data Multimedia Mining: Feature Extraction facing Volume, Velocity, and Variety
Chapter 3: Learning algorithms for large scale multimedia
3.1. Large Scale Video Understanding with Limited Training Labels
3.2. Multimodal fusion of big multimedia data
3.3. Large-Scale Social Multimedia Analysis
3.4. Privacy and audiovisual content: Protecting users as big multimedia data grows bigger
Chapter 4: Scalability in multimedia access
4.1. Data Storage and Management for Big Multimedia
4.2. Perceptual hashing for large-scale multimedia search
Chapter 5: Applications of large scale multimedia search
5.1. Image tagging with deep learning: Fine-grained Visual Analysis
5.2. Visually Exploring Millions of Images Using Image Maps and Graphs
5.3. Medical Decision Support Using Increasingly Large Multimodal Data Sets
Chapter 6: Conclusions and future trends
Benoit Huet is Assistant Professor in the Data Science Department of EURECOM, France. His current research interests include large scale multimedia content analysis, mining and indexing, multimodal fusion, and affective and socially-aware multimedia.
Edward Y. Chang has acted as the President of AI Research and Healthcare at HTC since 2012. Prior to his current post, he was a director of research at Google from 2006 to 2012, and a professor at the University of California, Santa Barbara, from 1999 to 2006. He is an IEEE Fellow for his contribution to scalable machine learning.
Ioannis Kompatsiaris is a Senior Researcher with the Information Technologies Institute (CERTH-ITI) in Greece, leading the Multimedia, Knowledge and Social Media Analytics Lab. His research interests include large-scale multimedia and social media analysis, knowledge structures and reasoning, eHealth, security and environmental applications.
