عنوان

Big data analytics for large-scale multimedia search /

(Number (ISBN

1119376971

(Number (ISBN

111937698X

(Number (ISBN

1119376998

(Number (ISBN

1119377005

(Number (ISBN

9781119376972

(Number (ISBN

9781119376989

(Number (ISBN

9781119376996

(Number (ISBN

9781119377009

Erroneous ISBN

9781119376972 (hardcover)

Title Proper

Big data analytics for large-scale multimedia search /

General Material Designation

[Book]

First Statement of Responsibility

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.

Place of Publication, Distribution, etc.

Hoboken, NJ, USA :

Name of Publisher, Distributor, etc.

Wiley,

Date of Publication, Distribution, etc.

[2018]

Date of Publication, Distribution, etc.

©2019

Date

1812

Specific Material Designation and Extent of Item

1 online resource

Text of Note

Includes bibliographical references and index.

Text of Note

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

Text of Note

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

Text of Note

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

Text of Note

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

Text of Note

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

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8

8

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8

Text of Note

"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.

Source for Acquisition/Subscription Address

Wiley

Stock Number

9781119377009

Title

Big data analytics for large-scale multimedia search

International Standard Book Number

9781119376972

Big data.

Multimedia data mining.

Big data.

COMPUTERS-- Databases-- Data Mining.

Multimedia data mining.

COM-- 021030

Number

Edition

Class number

Vrochidis, Stefanos,1975-

Chang, Edward Y.

Huet, Benoit

Kompatsiaris, Yiannis

Date of Transaction

20200822153431.0

Cataloguing Rules (Descriptive Conventions))

rda

Electronic name

[Book]

Y