Big Data Analytics for Large–Scale Multimedia Search - 
      Stefanos Vrochidis
    
      Benoit Huet
    
      Edward Y. Chang
    
      Ioannis Kompatsiaris

Big Data Analytics for Large–Scale Multimedia Search

Stefanos Vrochidis (Redaktør) ; Benoit Huet (Redaktør) ; Edward Y. Chang (Redaktør) ; Ioannis Kompatsiaris (Redaktør)

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. Les mer
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Vår pris: 1696,-

(Innbundet) Fri frakt!
Leveringstid: Sendes innen 21 dager

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.
FAKTA
Utgitt:
Forlag: Wiley-Blackwell
Innbinding: Innbundet
Språk: Engelsk
ISBN: 9781119376972
Format: 24 x 18 cm
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VURDERING
Gi vurdering
Les vurderinger

Introduction xv


List of Contributors xix


About the Companion Website xxiii


Part I Feature Extraction from Big Multimedia Data 1


1 Representation Learning on Large and Small Data 3
Chun-Nan Chou, Chuen-Kai Shie, Fu-Chieh Chang, Jocelyn Chang and Edward Y. Chang


1.1 Introduction 3


1.2 Representative Deep CNNs 5


1.2.1 AlexNet 6


1.2.1.1 ReLU Nonlinearity 6


1.2.1.2 Data Augmentation 7


1.2.1.3 Dropout 8


1.2.2 Network in Network 8


1.2.2.1 MLP Convolutional Layer 9


1.2.2.2 Global Average Pooling 9


1.2.3 VGG 10


1.2.3.1 Very Small Convolutional Filters 10


1.2.3.2 Multi-scale Training 11


1.2.4 GoogLeNet 11


1.2.4.1 Inception Modules 11


1.2.4.2 Dimension Reduction 12


1.2.5 ResNet 13


1.2.5.1 Residual Learning 13


1.2.5.2 Identity Mapping by Shortcuts 14


1.2.6 Observations and Remarks 15


1.3 Transfer Representation Learning 15


1.3.1 Method Specifications 17


1.3.2 Experimental Results and Discussion 18


1.3.2.1 Results of Transfer Representation Learning for OM 19


1.3.2.2 Results of Transfer Representation Learning for Melanoma 20


1.3.2.3 Qualitative Evaluation: Visualization 21


1.3.3 Observations and Remarks 23


1.4 Conclusions 24


References 25


2 Concept-Based and Event-Based Video Search in Large Video Collections 31
Foteini Markatopoulou, Damianos Galanopoulos, Christos Tzelepis, Vasileios Mezaris and Ioannis Patras


2.1 Introduction 32


2.2 Video preprocessing and Machine Learning Essentials 33


2.2.1 Video Representation 33


2.2.2 Dimensionality Reduction 34


2.3 Methodology for Concept Detection and Concept-Based Video Search 35


2.3.1 Related Work 35


2.3.2 Cascades for Combining Different Video Representations 37


2.3.2.1 Problem Definition and Search Space 37


2.3.2.2 Problem Solution 38


2.3.3 Multi-Task Learning for Concept Detection and Concept-Based Video Search 40


2.3.4 Exploiting Label Relations 41


2.3.5 Experimental Study 42


2.3.5.1 Dataset and Experimental Setup 42


2.3.5.2 Experimental Results 43


2.3.5.3 Computational Complexity 47


2.4 Methods for