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Big data analytics for large-scale m...

Chang, Edward Y.,

Big data analytics for large-scale multimedia search /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Big data analytics for large-scale multimedia search /Stefanos Vrochidis, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece, Benoit B. Huet, EURECOM, Sophia-Antipolis, France, Edward Y. Chang, HTC Research & Healthcare San Francisco, USA, Ioannis Kompatsiaris, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece.
Author:	Vrochidis, Stefanos,
other author:	Huet, Benoit,
Description:	xxiii, 348 pages ; 25 cm.
Subject:	Multimedia data mining.
Online resource:	https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2091369
Online resource:	https://public.ebookcentral.proquest.com/choice/publicfullrecord.aspx?p=5741210
Online resource:	https://doi.org/10.1002/9781119376996
Online resource:	http://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9781119376989
Online resource:	http://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9781119377009
Online resource:	https://ebookcentral.proquest.com/lib/canterbury/detail.action?docID=5741210
Online resource:	https://ebookcentral.proquest.com/lib/uvic/detail.action?docID=5741210
ISBN:	9781119376989

Big data analytics for large-scale multimedia search /
Vrochidis, Stefanos,1975-

Big data analytics for large-scale multimedia search /Stefanos Vrochidis, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece, Benoit B. Huet, EURECOM, Sophia-Antipolis, France, Edward Y. Chang, HTC Research & Healthcare San Francisco, USA, Ioannis Kompatsiaris, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece. - xxiii, 348 pages ; 25 cm.

Includes bibliographical references and index.

Cover; Title Page; Copyright; Contents; Introduction; List of Contributors; About the Companion Website; Part I Feature Extraction from Big Multimedia Data; Chapter 1 Representation Learning on Large and Small Data; 1.1 Introduction; 1.2 Representative Deep CNNs; 1.2.1 AlexNet; 1.2.1.1 ReLU Nonlinearity; 1.2.1.2 Data Augmentation; 1.2.1.3 Dropout; 1.2.2 Network in Network; 1.2.2.1 MLP Convolutional Layer; 1.2.2.2 Global Average Pooling; 1.2.3 VGG; 1.2.3.1 Very Small Convolutional Filters; 1.2.3.2 Multi-scale Training; 1.2.4 GoogLeNet; 1.2.4.1 Inception Modules; 1.2.4.2 Dimension Reduction

"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."--Provided by publisher.

ISBN: 9781119376989

Source: 9781119377009Wiley

LCCN: 2018037546

Nat. Bib. No.: GBB956590bnb

Nat. Bib. Agency Control No.: 019327505UkSubjects--Topical Terms:

790766
Multimedia data mining.
Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 005.7

GPO Item No.: cam 2200733 i 4500

Big data analytics for large-scale multimedia search /
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245 1 0 $a Big data analytics for large-scale multimedia search / $c Stefanos Vrochidis, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece, Benoit B. Huet, EURECOM, Sophia-Antipolis, France, Edward Y. Chang, HTC Research & Healthcare San Francisco, USA, Ioannis Kompatsiaris, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece.
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300 $a xxiii, 348 pages ; $c 25 cm.
336 $a text $b txt $2 rdacontent
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338 $a online resource $b nc $2 rdacarrier
504 $a Includes bibliographical references and index.
505 0 $a Cover; Title Page; Copyright; Contents; Introduction; List of Contributors; About the Companion Website; Part I Feature Extraction from Big Multimedia Data; Chapter 1 Representation Learning on Large and Small Data; 1.1 Introduction; 1.2 Representative Deep CNNs; 1.2.1 AlexNet; 1.2.1.1 ReLU Nonlinearity; 1.2.1.2 Data Augmentation; 1.2.1.3 Dropout; 1.2.2 Network in Network; 1.2.2.1 MLP Convolutional Layer; 1.2.2.2 Global Average Pooling; 1.2.3 VGG; 1.2.3.1 Very Small Convolutional Filters; 1.2.3.2 Multi-scale Training; 1.2.4 GoogLeNet; 1.2.4.1 Inception Modules; 1.2.4.2 Dimension Reduction
505 8 $a 1.2.5 ResNet1.2.5.1 Residual Learning; 1.2.5.2 Identity Mapping by Shortcuts; 1.2.6 Observations and Remarks; 1.3 Transfer Representation Learning; 1.3.1 Method Specifications; 1.3.2 Experimental Results and Discussion; 1.3.2.1 Results of Transfer Representation Learning for OM; 1.3.2.2 Results of Transfer Representation Learning for Melanoma; 1.3.2.3 Qualitative Evaluation: Visualization; 1.3.3 Observations and Remarks; 1.4 Conclusions; References; Chapter 2 Concept-Based and Event-Based Video Search in Large Video Collections; 2.1 Introduction
505 8 $a 2.2 Video preprocessing and Machine Learning Essentials2.2.1 Video Representation; 2.2.2 Dimensionality Reduction; 2.3 Methodology for Concept Detection and Concept-Based Video Search; 2.3.1 Related Work; 2.3.2 Cascades for Combining Different Video Representations; 2.3.2.1 Problem Definition and Search Space; 2.3.2.2 Problem Solution; 2.3.3 Multi-Task Learning for Concept Detection and Concept-Based Video Search; 2.3.4 Exploiting Label Relations; 2.3.5 Experimental Study; 2.3.5.1 Dataset and Experimental Setup; 2.3.5.2 Experimental Results; 2.3.5.3 Computational Complexity
505 8 $a 2.4 Methods for Event Detection and Event-Based Video Search2.4.1 Related Work; 2.4.2 Learning from Positive Examples; 2.4.3 Learning Solely from Textual Descriptors: Zero-Example Learning; 2.4.4 Experimental Study; 2.4.4.1 Dataset and Experimental Setup; 2.4.4.2 Experimental Results: Learning from Positive Examples; 2.4.4.3 Experimental Results: Zero-Example Learning; 2.5 Conclusions; 2.6 Acknowledgments; References; Chapter 3 Big Data Multimedia Mining: Feature Extraction Facing Volume, Velocity, and Variety; 3.1 Introduction; 3.2 Scalability through Parallelization
505 8 $a 3.2.1 Process Parallelization3.2.2 Data Parallelization; 3.3 Scalability through Feature Engineering; 3.3.1 Feature Reduction through Spatial Transformations; 3.3.2 Laplacian Matrix Representation; 3.3.3 Parallel latent Dirichlet allocation and bag of words; 3.4 Deep Learning-Based Feature Learning; 3.4.1 Adaptability that Conquers both Volume and Velocity; 3.4.2 Convolutional Neural Networks; 3.4.3 Recurrent Neural Networks; 3.4.4 Modular Approach to Scalability; 3.5 Benchmark Studies; 3.5.1 Dataset; 3.5.2 Spectrogram Creation; 3.5.3 CNN-Based Feature Extraction; 3.5.4 Structure of the CNNs
520 $a "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."--Provided by publisher.
588 $a Description based on print version record and CIP data provided by publisher; resource not viewed.
650 0 $a Multimedia data mining. $3 790766
650 0 $a Big data. $3 609582
650 7 $a COMPUTERS $x Databases $x Data Mining. $2 bisacsh $3 858182
655 0 $a Electronic books. $2 local. $3 214472
700 1 $a Huet, Benoit, $e author. $3 858179
700 1 $a Chang, Edward Y., $e author. $3 858180
700 1 $a Kompatsiaris, Yiannis, $e author. $3 858181
776 0 8 $i Print version: $a Vrochidis, Stefanos, 1975- author. $t Big data analytics for large-scale multimedia search $d Hoboken, NJ, USA : Wiley, [2018] $z 9781119376972 $w (DLC) 2018035613
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https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2091369
https://public.ebookcentral.proquest.com/choice/publicfullrecord.aspx?p=5741210
https://doi.org/10.1002/9781119376996
http://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9781119376989
http://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9781119377009
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