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El. knyga: Big Data Analytics for Large-Scale Multimedia Search

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  • Formatas: EPUB+DRM
  • Išleidimo metai: 18-Mar-2019
  • Leidėjas: John Wiley & Sons Inc
  • Kalba: eng
  • ISBN-13: 9781119377009
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Big Data Analytics for Large-Scale Multimedia SearchDidesnis paveikslėlis
  • Formatas: EPUB+DRM
  • Išleidimo metai: 18-Mar-2019
  • Leidėjas: John Wiley & Sons Inc
  • Kalba: eng
  • ISBN-13: 9781119377009
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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.

Introduction xv
List of Contributors
xix
About the Companion Website xxiii
Part I Feature Extraction from Big Multimedia Data
1(88)
1 Representation Learning on Large and Small Data
3(28)
Chun-Nan Chou
Chuen-Kai Shie
Fu-Chieh Chang
Jocelyn Chang
Edward Y. Chang
1.1 Introduction
3(2)
1.2 Representative Deep CNNs
5(10)
1.2.1 AlexNet
6(1)
1.2.1.1 ReLU Nonlinearity
6(1)
1.2.1.2 Data Augmentation
7(1)
1.2.1.3 Dropout
8(1)
1.2.2 Network in Network
8(1)
1.2.2.1 MLP Convolutional Layer
9(1)
1.2.2.2 Global Average Pooling
9(1)
1.2.3 VGG
10(1)
1.2.3.1 Very Small Convolutional Filters
10(1)
1.2.3.2 Multi-scale Training
11(1)
1.2.4 GoogleNet
11(1)
1.2.4.1 Inception Modules
11(1)
1.2.4.2 Dimension Reduction
12(1)
1.2.5 ResNet
13(1)
1.2.5.1 Residual Learning
13(1)
1.2.5.2 Identity Mapping by Shortcuts
14(1)
1.2.6 Observations and Remarks
15(1)
1.3 Transfer Representation Learning
15(9)
1.3.1 Method Specifications
17(1)
1.3.2 Experimental Results and Discussion
18(1)
1.3.2.1 Results of Transfer Representation Learning for OM
19(1)
1.3.2.2 Results of Transfer Representation Learning for Melanoma
20(1)
1.3.2.3 Qualitative Evaluation: Visualization
21(2)
1.3.3 Observations and Remarks
23(1)
1.4 Conclusions
24(1)
References
25(6)
2 Concept-Based and Event-Based Video Search in Large Video Collections
31(30)
Foteini Markatopoulou
Damianos Galanopoulos
Christos Tzelepis
Vasileios Mezaris
Ioannis Patras
2.1 Introduction
32(1)
2.2 Video preprocessing and Machine Learning Essentials
33(2)
2.2.1 Video Representation
33(1)
2.2.2 Dimensionality Reduction
34(1)
2.3 Methodology for Concept Detection and Concept-Based Video Search
35(13)
2.3.1 Related Work
35(2)
2.3.2 Cascades for Combining Different Video Representations
37(1)
2.3.2.1 Problem Definition and Search Space
37(1)
2.3.2.2 Problem Solution
38(2)
2.3.3 Multi-Task Learning for Concept Detection and Concept-Based Video Search
40(1)
2.3.4 Exploiting Label Relations
41(1)
2.3.5 Experimental Study
42(1)
2.3.5.1 Dataset and Experimental Setup
42(1)
2.3.5.2 Experimental Results
43(4)
2.3.5.3 Computational Complexity
47(1)
2.4 Methods for Event Detection and Event-Based Video Search
48(6)
2.4.1 Related Work
48(1)
2.4.2 Learning from Positive Examples
49(1)
2.4.3 Learning Solely from Textual Descriptors: Zero-Example Learning
50(2)
2.4.4 Experimental Study
52(1)
2.4.4.1 Dataset and Experimental Setup
52(1)
2.4.4.2 Experimental Results: Learning from Positive Examples
53(1)
2.4.4.3 Experimental Results: Zero-Example Learning
53(1)
2.5 Conclusions
54(1)
2.6 Acknowledgments
55(1)
References
55(6)
3 Big Data Multimedia Mining: Feature Extraction Facing Volume, Velocity, and Variety
61(28)
Vedhas Pandit
Shahin Amiriparian
Maximilian Schmitt
Amr Mousa
Bjorn Schuller
3.1 Introduction
61(3)
3.2 Scalability through Parallelization
64(1)
3.2.1 Process Parallelization
64(1)
3.2.2 Data Parallelization
64(1)
3.3 Scalability through Feature Engineering
65(3)
3.3.1 Feature Reduction through Spatial Transformations
66(1)
3.3.2 Laplacian Matrix Representation
66(2)
3.3.3 Parallel latent Dirichlet allocation and bag of words
68(1)
3.4 Deep Learning-Based Feature Learning
68(8)
3.4.1 Adaptability that Conquers both Volume and Velocity
70(2)
3.4.2 Convolutional Neural Networks
72(1)
3.4.3 Recurrent Neural Networks
73(1)
3.4.4 Modular Approach to Scalability
74(2)
3.5 Benchmark Studies
76(5)
3.5.1 Dataset
76(1)
3.5.2 Spectrogram Creation
77(1)
3.5.3 CNN-Based Feature Extraction
77(1)
3.5.4 Structure of the CNNs
78(1)
3.5.5 Process Parallelization
79(1)
3.5.6 Results
80(1)
3.6 Closing Remarks
81(1)
3.7 Acknowledgements
82(1)
References
82(7)
Part II Learning Algorithms for Large-Scale Multimedia
89(120)
4 Large-Scale Video Understanding with Limited Training Labels
91(30)
Jingkuan Song
Xu Zhao
Lianli Gao
Liangliang Cao
4.1 Introduction
91(1)
4.2 Video Retrieval with Hashing
91(12)
4.2.1 Overview
91(2)
4.2.2 Unsupervised Multiple Feature Hashing
93(1)
4.2.2.1 Framework
93(1)
4.2.2.2 The Objective Function of MFH
93(2)
4.2.2.3 Solution of MFH
95(1)
4.2.2.3.1 Complexity Analysis
96(1)
4.2.3 Submodular Video Hashing
97(1)
4.2.3.1 Framework
97(1)
4.2.3.2 Video Pooling
97(1)
4.2.3.3 Submodular Video Hashing
98(1)
4.2.4 Experiments
99(1)
4.2.4.1 Experiment Settings
99(1)
4.2.4.1.1 Video Datasets
99(1)
4.2.4.1.2 Visual Features
99(1)
4.2.4.1.3 Algorithms for Comparison
100(1)
4.2.4.2 Results
100(1)
4.2.4.2.1 CC_WEB_VIDEO
100(1)
4.2.4.2.2 Combined Dataset
100(1)
4.2.4.3 Evaluation of SVH
101(1)
4.2.4.3.1 Results
102(1)
4.3 Graph-Based Model for Video Understanding
103(13)
4.3.1 Overview
103(1)
4.3.2 Optimized Graph Learning for Video Annotation
104(1)
4.3.2.1 Framework
104(1)
4.3.2.2 OGL
104(1)
4.3.2.2.1 Terms and Notations
104(1)
4.3.2.2.2 Optimal Graph-Based SSL
105(1)
4.3.2.2.3 Iterative Optimization
106(1)
4.3.3 Context Association Model for Action Recognition
107(1)
4.3.3.1 Context Memory
108(1)
4.3.4 Graph-based Event Video Summarization
109(1)
4.3.4.1 Framework
109(1)
4.3.4.2 Temporal Alignment
110(1)
4.3.5 TGIF: A New Dataset and Benchmark on Animated GIF Description
111(1)
4.3.5.1 Data Collection
111(1)
4.3.5.2 Data Annotation
112(2)
4.3.6 Experiments
114(1)
4.3.6.1 Experimental Settings
114(1)
4.3.6.1.1 Datasets
114(1)
4.3.6.1.2 Features
114(1)
4.3.6.1.3 Baseline Methods and Evaluation Metrics
114(1)
4.3.6.2 Results
115(1)
4.4 Conclusions and Future Work
116(1)
References
116(5)
5 Multimodal Fusion of Big Multimedia Data
121(36)
Mas Gialampoukidis
Elisavet Chatzilari
Spiros Nikolopoulos
Stefanos Vrochidis
Ioannis Kompatsiaris
5.1 Multimodal Fusion in Multimedia Retrieval
122(8)
5.1.1 Unsupervised Fusion in Multimedia Retrieval
123(1)
5.1.1.1 Linear and Non-linear Similarity Fusion
123(1)
5.1.1.2 Cross-modal Fusion of Similarities
124(1)
5.1.1.3 Random Walks and Graph-based Fusion
124(2)
5.1.1.4 A Unifying Graph-based Model
126(1)
5.1.2 Partial Least Squares Regression
127(1)
5.1.3 Experimental Comparison
128(1)
5.1.3.1 Dataset Description
128(1)
5.1.3.2 Settings
129(1)
5.1.3.3 Results
129(1)
5.1 A Late Fusion of Multiple Multimedia Rankings
130(2)
5.1.4.1 Score Fusion
131(1)
5.1.4.2 Rank Fusion
132(1)
5.1.4.2.1 Borda Count Fusion
132(1)
5.1.4.2.2 Reciprocal Rank Fusion
132(1)
5.1.4.2.3 Condorcet Fusion
132(1)
5.2 Multimodal Fusion in Multimedia Classification
132(19)
5.2.1 Related Literature
134(2)
5.2.2 Problem Formulation
136(1)
5.2.3 Probabilistic Fusion in Active Learning
137(1)
5.2.3.1 If P(S=0|V,T)≠0
138(1)
5.2.3.2 If P(S=0|V,T)≠0
138(1)
5.2.3.3 Incorporating Informativeness in the Selection (P(S\T))
139(1)
5.2.3 A Measuring Oracle's Confidence (P(S\T))
139(1)
5.2.3.5 Re-training
140(1)
5.2.4 Experimental Comparison
141(1)
5.2.4.1 Datasets
141(1)
5.2.4.2 Settings
142(1)
5.2.4.3 Results
143(1)
5.2.4.3.1 Expanding with Positive, Negative or Both
143(2)
5.2.4.3.2 Comparing with Sample Selection Approaches
145(2)
5.2.4.3.3 Comparing with Fusion Approaches
147(1)
5.2.4.3.4 Parameter Sensitivity Investigation
147(1)
5.2.4.3.5 Comparing with Existing Methods
148(3)
5.3 Conclusions
151(1)
References
152(5)
6 Large-Scale Social Multimedia Analysis
157(26)
Benjamin Bischke
Damian Borth
Andreas Dengel
6.1 Social Multimedia in Social Media Streams
157(10)
6.1.1 Social Multimedia
157(1)
6.1.2 Social Multimedia Streams
158(2)
6.1.3 Analysis of the Twitter Firehose
160(1)
6.1.3.1 Dataset: Overview
160(1)
6.1.3.2 Linked Resource Analysis
160(2)
6.1.3.3 Image Content Analysis
162(2)
6.1.3.4 Geographic Analysis
164(2)
6.1.3.5 Textual Analysis
166(1)
6.2 Large-Scale Analysis of Social Multimedia
167(3)
6.2.1 Large-Scale Processing of Social Multimedia Analysis
167(1)
6.2.1.1 Batch-Processing Frameworks
167(1)
6.2.1.2 Stream-Processing Frameworks
168(1)
6.2.1.3 Distributed Processing Frameworks
168(1)
6.2.2 Analysis of Social Multimedia
169(1)
6.2.2.1 Analysis of Visual Content
169(1)
6.2.2.2 Analysis of Textual Content
169(1)
6.2.2.3 Analysis of Geographical Content
170(1)
6.2.2.4 Analysis of User Content
170(1)
6.3 Large-Scale Multimedia Opinion Mining System
170(8)
6.3.1 System Overview
171(1)
6.3.2 Implementation Details
171(1)
6.3.2.1 Social Media Data Crawler
172(1)
6.3.2.2 Social Multimedia Analysis
173(1)
6.3.2.3 Analysis of Visual Content
174(1)
6.3.3 Evaluations: Analysis of Visual Content
175(1)
6.3.3.1 Filtering of Synthetic Images
175(2)
6.3.3.2 Near-Duplicate Detection
177(1)
6.4 Conclusion
178(1)
References
179(4)
7 Privacy and Audiovisual Content: Protecting Users as Big Multimedia Data Grows Bigger
183(26)
Martha Larson
Jaeyoung Choi
Manel Slokom
Zekeriya Erkin
Gerald Frledland
Arjen P. de Vries
7.1 Introduction
183(5)
7.1.1 The Dark Side of Big Multimedia Data
184(1)
7.1.2 Defining Multimedia Privacy
184(4)
7.2 Protecting User Privacy
188(4)
7.2.1 What to Protect
188(1)
7.2.2 How to Protect
189(2)
7.2.3 Threat Models
191(1)
7.3 Multimedia Privacy
192(4)
7.3.1 Privacy and Multimedia Big Data
192(2)
7.3.2 Privacy Threats of Multimedia Data
194(1)
7.3.2.1 Audio Data
194(1)
7.3.2.2 Visual Data
195(1)
7.3.2.3 Multimodal Threats
195(1)
7.4 Privacy-Related Multimedia Analysis Research
196(3)
7.4.1 Multimedia Analysis Filters
196(2)
7.4.2 Multimedia Content Masking
198(1)
7.5 The Larger Research Picture
199(3)
7.5.1 Multimedia Security and Trust
199(1)
7.5.2 Data Privacy
200(2)
7.6 Outlook on Multimedia Privacy Challenges
202(3)
7.6.1 Research Challenges
202(1)
7.6.1.1 Multimedia Analysis
202(1)
7.6.1.2 Data
202(1)
7.6.1.3 Users
203(1)
7.6.2 Research Reorientation
204(1)
7.6.2.1 Professional Paranoia
204(1)
7.6.2.2 Privacy as a Priority
204(1)
7.6.2.3 Privacy in Parallel
205(1)
References
205(4)
Part III Scalability in Multimedia Access
209(58)
8 Data Storage and Management for Big Multimedia
211(28)
Bjorn Por Jonsson
Gylfi Por Gudmundsson
Laurent Amsaleg
Philippe Bonnet
8.1 Introduction
211(6)
8.1.1 Multimedia Applications and Scale
212(1)
8.1.2 Big Data Management
213(1)
8.1.3 System Architecture Outline
213(1)
8.1.4 Metadata Storage Architecture
214(1)
8.1.4.1 Lambda Architecture
214(1)
8.1.4.2 Storage Layer
215(1)
8.1.4.3 Processing Layer
216(1)
8.1.4.4 Serving Layer
216(1)
8.1.4.5 Dynamic Data
216(1)
8.1.5 Summary and
Chapter Outline
217(1)
8.2 Media Storage
217(5)
8.2.1 Storage Hierarchy
217(1)
8.2.1.1 Secondary Storage
218(1)
8.2.1.2 The Five-Minute Rule
218(1)
8.2.1.3 Emerging Trends for Local Storage
219(1)
8.2.2 Distributed Storage
220(1)
8.2.2.1 Distributed Hash Tables
221(1)
8.2.2.2 The CAP Theorem and the PACELC Formulation
221(1)
8.2.2.3 The Hadoop Distributed File System
221(1)
8.2.2.4 Ceph
222(1)
8.2.3 Discussion
222(1)
8.3 Processing Media
222(4)
8.3.1 Metadata Extraction
223(1)
8.3.2 Batch Processing
223(1)
8.3.2.1 Map-Reduce and Hadoop
224(1)
8.3.2.2 Spark
225(1)
8.3.2.3 Comparison
226(1)
8.3.3 Stream Processing
226(1)
8.4 Multimedia Delivery
226(4)
8.4.1 Distributed In-Memory Buffering
227(1)
8.4.1.1 Memcached and Redis
227(1)
8.4.1.2 Alluxio
227(1)
8.4.1.3 Content Distribution Networks
228(1)
8.4.2 Metadata Retrieval and NoSQL Systems
228(1)
8.4.2.1 Key-Value Stores
229(1)
8.4.2.2 Document Stores
229(1)
8.4.2.3 Wide Column Stores
229(1)
8.4.2.4 Graph Stores
229(1)
8.4.3 Discussion
229(1)
8.5 Case Studies: Facebook
230(1)
8.5.1 Data Popularity: Hot, Warm or Cold
230(1)
8.5.2 Mentions Live
231(1)
8.6 Conclusions and Future Work
231(1)
8.6.1 Acknowledgments
232(1)
References
232(7)
9 Perceptual Hashing for Large-Scale Multimedia Search
239(28)
Li Weng
I-Hong Jhuo
Wen-Huang Cheng
9.1 Introduction
240(5)
9.1.1 Related work
240(1)
9.1.2 Definitions and Properties of Perceptual Hashing
241(2)
9.1.3 Multimedia Search using Perceptual Hashing
243(1)
9.1.4 Applications of Perceptual Hashing
243(1)
9.1.5 Evaluating Perceptual Hash Algorithms
244(1)
9.2 Unsupervised Perceptual Hash Algorithms
245(5)
9.2.1 Spectral Hashing
245(1)
9.2.2 Iterative Quantization
246(1)
9.2.3 K-Means Hashing
247(2)
9.2.4 Kernelized Locality Sensitive Hashing
249(1)
9.3 Supervised Perceptual Hash Algorithms
250(7)
9.3.1 Semi-Supervised Hashing
250(2)
9.3.2 Kernel-Based Supervised Hashing
252(1)
9.3.3 Restricted Boltzmann Machine-Based Hashing
253(2)
9.3.4 Supervised Semantic-Preserving Deep Hashing
255(2)
9.4 Constructing Perceptual Hash Algorithms
257(3)
9.4.1 Two-Step Hashing
257(1)
9.4.2 Hash Bit Selection
258(2)
9.5 Conclusion and Discussion
260(1)
References
261(6)
Part IV Applications of Large-Scale Multimedia Search
267(63)
10 Image Tagging with Deep Learning: Fine-Grained Visual Analysis
269(20)
Jianlong Fu
Tao Mei
10.1 Introduction
269(1)
10.2 Basic Deep Learning Models
270(2)
10.3 Deep Image Tagging for Fine-Grained Image Recognition
272(9)
10.3.1 Attention Proposal Network
274(1)
10.3.2 Classification and Ranking
275(1)
10.3.3 Multi-Scale Joint Representation
276(1)
10.3.4 Implementation Details
276(1)
10.3.5 Experiments on CUB-200-2011
277(3)
10.3.6 Experiments on Stanford Dogs
280(1)
10.4 Deep Image Tagging for Fine-Grained Sentiment Analysis
281(3)
10.4.1 Learning Deep Sentiment Representation
282(1)
10.4.2 Sentiment Analysis
283(1)
10.4.3 Experiments on SentiBank
283(1)
10.5 Conclusion
284(1)
References
285(4)
11 Visually Exploring Millions of Images using Image Maps and Graphs
289(28)
Kai Uwe Barthel
Nico Hezel
11.1 Introduction and Related Work
290(3)
11.2 Algorithms for Image Sorting
293(2)
11.2.1 Self-Organizing Maps
293(1)
11.2.2 Self-Sorting Maps
294(1)
11.2.3 Evolutionary Algorithms
295(1)
11.3 Improving SOMs for Image Sorting
295(3)
11.3.1 Reducing SOM Sorting Complexity
295(2)
11.3.2 Improving SOM Projection Quality
297(1)
11.3.3 Combining SOMs and SSMs
297(1)
11.4 Quality Evaluation of Image Sorting Algorithms
298(3)
11.4.1 Analysis of SOMs
298(1)
11.4.2 Normalized Cross-Correlation
299(1)
11.4.3 A New Image Sorting Quality Evaluation Scheme
299(2)
11.5 2D Sorting Results
301(3)
11.5.1 Image Test Sets
301(1)
11.5.2 Experiments
302(2)
11.6 Demo System for Navigating 2D Image Maps
304(2)
11.7 Graph-Based Image Browsing
306(6)
11.7.1 Generating Semantic Image Features
306(1)
11.7.2 Building the Image Graph
307(3)
11.7.3 Visualizing and Navigating the Graph
310(2)
11.7 A Prototype for Image Graph Navigation
312(1)
11.8 Conclusion and Future Work
313(1)
References
313(4)
12 Medical Decision Support Using Increasingly Large Multimodal Data Sets
317(13)
Henning Muller
Devrim Unay
12.1 Introduction
317(3)
12.2 Methodology for Reviewing the Literature in this chapter
320(1)
12.3 Data, Ground Truth, and Scientific Challenges
321(2)
12.3.1 Data Annotation and Ground Truthing
321(1)
12.3.2 Scientific Challenges and Evaluation as a Service
321(1)
12.3.3 Other Medical Data Resources Available
322(1)
12.4 Techniques used for Multimodal Medical Decision Support
323(3)
12.4.1 Visual and Non-Visual Features Describing the Image Content
323(1)
12.4.2 General Machine Learning and Deep Learning
323(3)
12.5 Application Types of Image-Based Decision Support
326(2)
12.5.1 Localization
326(1)
12.5.2 Segmentation
326(1)
12.5.3 Classification
327(1)
12.5.4 Prediction
327(1)
12.5.5 Retrieval
327(1)
12.5.6 Automatic Image Annotation
328(1)
12.5.7 Other Application Types
328(1)
12.6 Discussion on Multimodal Medical Decision Support
328(1)
12.7 Outlook or the Next Steps of Multimodal Medical Decision Support
329(1)
References 330(7)
Conclusions and Future Trends 337(2)
Index 339
Stefanos Vrochidis is a Senior Researcher with the Information Technologies Institute (CERTH-ITI) in Greece. His research interests include multimedia retrieval, semantic multimedia analysis, multimodal big data analytics, web data mining, multimodal interaction and security applications.



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.
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Pastovi nuoroda: https://www.kriso.lt/db/97811193770096e.html
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