Big Data Analytics for Large-Scale Multimedia Search

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.

Table of Contents:
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 Event Detection and Event-Based Video Search 48 2.4.1 Related Work 48 2.4.2 Learning from Positive Examples 49 2.4.3 Learning Solely from Textual Descriptors: Zero-Example Learning 50 2.4.4 Experimental Study 52 2.4.4.1 Dataset and Experimental Setup 52 2.4.4.2 Experimental Results: Learning from Positive Examples 53 2.4.4.3 Experimental Results: Zero-Example Learning 53 2.5 Conclusions 54 2.6 Acknowledgments 55 References 55 3 Big Data Multimedia Mining: Feature Extraction Facing Volume, Velocity, and Variety 61 Vedhas Pandit, Shahin Amiriparian, Maximilian Schmitt, Amr Mousa and Björn Schuller 3.1 Introduction 61 3.2 Scalability through Parallelization 64 3.2.1 Process Parallelization 64 3.2.2 Data Parallelization 64 3.3 Scalability through Feature Engineering 65 3.3.1 Feature Reduction through Spatial Transformations 66 3.3.2 Laplacian Matrix Representation 66 3.3.3 Parallel latent Dirichlet allocation and bag of words 68 3.4 Deep Learning-Based Feature Learning 68 3.4.1 Adaptability that Conquers both Volume and Velocity 70 3.4.2 Convolutional Neural Networks 72 3.4.3 Recurrent Neural Networks 73 3.4.4 Modular Approach to Scalability 74 3.5 Benchmark Studies 76 3.5.1 Dataset 76 3.5.2 Spectrogram Creation 77 3.5.3 CNN-Based Feature Extraction 77 3.5.4 Structure of the CNNs 78 3.5.5 Process Parallelization 79 3.5.6 Results 80 3.6 Closing Remarks 81 3.7 Acknowledgements 82 References 82 Part II Learning Algorithms for Large-Scale Multimedia 89 4 Large-Scale Video Understanding with Limited Training Labels 91 Jingkuan Song, Xu Zhao, Lianli Gao and Liangliang Cao 4.1 Introduction 91 4.2 Video Retrieval with Hashing 91 4.2.1 Overview 91 4.2.2 Unsupervised Multiple Feature Hashing 93 4.2.2.1 Framework 93 4.2.2.2 The Objective Function of MFH 93 4.2.2.3 Solution of MFH 95 4.2.2.3.1 Complexity Analysis 96 4.2.3 Submodular Video Hashing 97 4.2.3.1 Framework 97 4.2.3.2 Video Pooling 97 4.2.3.3 Submodular Video Hashing 98 4.2.4 Experiments 99 4.2.4.1 Experiment Settings 99 4.2.4.1.1 Video Datasets 99 4.2.4.1.2 Visual Features 99 4.2.4.1.3 Algorithms for Comparison 100 4.2.4.2 Results 100 4.2.4.2.1 CC_WEB_VIDEO 100 4.2.4.2.2 Combined Dataset 100 4.2.4.3 Evaluation of SVH 101 4.2.4.3.1 Results 102 4.3 Graph-Based Model for Video Understanding 103 4.3.1 Overview 103 4.3.2 Optimized Graph Learning for Video Annotation 104 4.3.2.1 Framework 104 4.3.2.2 OGL 104 4.3.2.2.1 Terms and Notations 104 4.3.2.2.2 Optimal Graph-Based SSL 105 4.3.2.2.3 Iterative Optimization 106 4.3.3 Context Association Model for Action Recognition 107 4.3.3.1 Context Memory 108 4.3.4 Graph-based Event Video Summarization 109 4.3.4.1 Framework 109 4.3.4.2 Temporal Alignment 110 4.3.5 TGIF: A New Dataset and Benchmark on Animated GIF Description 111 4.3.5.1 Data Collection 111 4.3.5.2 Data Annotation 112 4.3.6 Experiments 114 4.3.6.1 Experimental Settings 114 4.3.6.1.1 Datasets 114 4.3.6.1.2 Features 114 4.3.6.1.3 Baseline Methods and Evaluation Metrics 114 4.3.6.2 Results 115 4.4 Conclusions and Future Work 116 References 116 5 Multimodal Fusion of Big Multimedia Data 121 Ilias Gialampoukidis, Elisavet Chatzilari, Spiros Nikolopoulos, Stefanos Vrochidis and Ioannis Kompatsiaris 5.1 Multimodal Fusion in Multimedia Retrieval 122 5.1.1 Unsupervised Fusion in Multimedia Retrieval 123 5.1.1.1 Linear and Non-linear Similarity Fusion 123 5.1.1.2 Cross-modal Fusion of Similarities 124 5.1.1.3 Random Walks and Graph-based Fusion 124 5.1.1.4 A Unifying Graph-based Model 126 5.1.2 Partial Least Squares Regression 127 5.1.3 Experimental Comparison 128 5.1.3.1 Dataset Description 128 5.1.3.2 Settings 129 5.1.3.3 Results 129 5.1.4 Late Fusion of Multiple Multimedia Rankings 130 5.1.4.1 Score Fusion 131 5.1.4.2 Rank Fusion 132 5.1.4.2.1 Borda Count Fusion 132 5.1.4.2.2 Reciprocal Rank Fusion 132 5.1.4.2.3 Condorcet Fusion 132 5.2 Multimodal Fusion in Multimedia Classification 132 5.2.1 Related Literature 134 5.2.2 Problem Formulation 136 5.2.3 Probabilistic Fusion in Active Learning 137 5.2.3.1 If P(S=0|V,T)≠0: 138 5.2.3.2 If P(S=0|V,T)≠0: 138 5.2.3.3 Incorporating Informativeness in the Selection (P(S|V)) 139 5.2.3.4 Measuring Oracle’s Confidence (P(S|T)) 139 5.2.3.5 Re-training 140 5.2.4 Experimental Comparison 141 5.2.4.1 Datasets 141 5.2.4.2 Settings 142 5.2.4.3 Results 143 5.2.4.3.1 Expanding with Positive, Negative or Both 143 5.2.4.3.2 Comparing with Sample Selection Approaches 145 5.2.4.3.3 Comparing with Fusion Approaches 147 5.2.4.3.4 Parameter Sensitivity Investigation 147 5.2.4.3.5 Comparing with Existing Methods 148 5.3 Conclusions 151 References 152 6 Large-Scale Social Multimedia Analysis 157 Benjamin Bischke, Damian Borth and Andreas Dengel 6.1 Social Multimedia in Social Media Streams 157 6.1.1 Social Multimedia 157 6.1.2 Social Multimedia Streams 158 6.1.3 Analysis of the Twitter Firehose 160 6.1.3.1 Dataset: Overview 160 6.1.3.2 Linked Resource Analysis 160 6.1.3.3 Image Content Analysis 162 6.1.3.4 Geographic Analysis 164 6.1.3.5 Textual Analysis 166 6.2 Large-Scale Analysis of Social Multimedia 167 6.2.1 Large-Scale Processing of Social Multimedia Analysis 167 6.2.1.1 Batch-Processing Frameworks 167 6.2.1.2 Stream-Processing Frameworks 168 6.2.1.3 Distributed Processing Frameworks 168 6.2.2 Analysis of Social Multimedia 169 6.2.2.1 Analysis of Visual Content 169 6.2.2.2 Analysis of Textual Content 169 6.2.2.3 Analysis of Geographical Content 170 6.2.2.4 Analysis of User Content 170 6.3 Large-Scale Multimedia Opinion Mining System 170 6.3.1 System Overview 171 6.3.2 Implementation Details 171 6.3.2.1 Social Media Data Crawler 171 6.3.2.2 Social Multimedia Analysis 173 6.3.2.3 Analysis of Visual Content 174 6.3.3 Evaluations: Analysis of Visual Content 175 6.3.3.1 Filtering of Synthetic Images 175 6.3.3.2 Near-Duplicate Detection 177 6.4 Conclusion 178 References 179 7 Privacy and Audiovisual Content: Protecting Users as Big Multimedia Data Grows Bigger 183 Martha Larson, Jaeyoung Choi, Manel Slokom, Zekeriya Erkin, Gerald Friedland and Arjen P. de Vries 7.1 Introduction 183 7.1.1 The Dark Side of Big Multimedia Data 184 7.1.2 Defining Multimedia Privacy 184 7.2 Protecting User Privacy 188 7.2.1 What to Protect 188 7.2.2 How to Protect 189 7.2.3 Threat Models 191 7.3 Multimedia Privacy 192 7.3.1 Privacy and Multimedia Big Data 192 7.3.2 Privacy Threats of Multimedia Data 194 7.3.2.1 Audio Data 194 7.3.2.2 Visual Data 195 7.3.2.3 Multimodal Threats 195 7.4 Privacy-Related Multimedia Analysis Research 196 7.4.1 Multimedia Analysis Filters 196 7.4.2 Multimedia Content Masking 198 7.5 The Larger Research Picture 199 7.5.1 Multimedia Security and Trust 199 7.5.2 Data Privacy 200 7.6 Outlook on Multimedia Privacy Challenges 202 7.6.1 Research Challenges 202 7.6.1.1 Multimedia Analysis 202 7.6.1.2 Data 202 7.6.1.3 Users 203 7.6.2 Research Reorientation 204 7.6.2.1 Professional Paranoia 204 7.6.2.2 Privacy as a Priority 204 7.6.2.3 Privacy in Parallel 205 References 205 Part III Scalability in Multimedia Access 209 8 Data Storage and Management for Big Multimedia 211 Björn Þór Jónsson, Gylfi Þór Gudmundsson, Laurent Amsaleg and Philippe Bonnet 8.1 Introduction 211 8.1.1 Multimedia Applications and Scale 212 8.1.2 Big Data Management 213 8.1.3 System Architecture Outline 213 8.1.4 Metadata Storage Architecture 214 8.1.4.1 Lambda Architecture 214 8.1.4.2 Storage Layer 215 8.1.4.3 Processing Layer 216 8.1.4.4 Serving Layer 216 8.1.4.5 Dynamic Data 216 8.1.5 Summary and Chapter Outline 217 8.2 Media Storage 217 8.2.1 Storage Hierarchy 217 8.2.1.1 Secondary Storage 218 8.2.1.2 The Five-Minute Rule 218 8.2.1.3 Emerging Trends for Local Storage 219 8.2.2 Distributed Storage 220 8.2.2.1 Distributed Hash Tables 221 8.2.2.2 The CAP Theorem and the PACELC Formulation 221 8.2.2.3 The Hadoop Distributed File System 221 8.2.2.4 Ceph 222 8.2.3 Discussion 222 8.3 Processing Media 222 8.3.1 Metadata Extraction 223 8.3.2 Batch Processing 223 8.3.2.1 Map-Reduce and Hadoop 224 8.3.2.2 Spark 225 8.3.2.3 Comparison 226 8.3.3 Stream Processing 226 8.4 Multimedia Delivery 226 8.4.1 Distributed In-Memory Buffering 227 8.4.1.1 Memcached and Redis 227 8.4.1.2 Alluxio 227 8.4.1.3 Content Distribution Networks 228 8.4.2 Metadata Retrieval and NoSQL Systems 228 8.4.2.1 Key-Value Stores 229 8.4.2.2 Document Stores 229 8.4.2.3 Wide Column Stores 229 8.4.2.4 Graph Stores 229 8.4.3 Discussion 229 8.5 Case Studies: Facebook 230 8.5.1 Data Popularity: Hot, Warm or Cold 230 8.5.2 Mentions Live 231 8.6 Conclusions and Future Work 231 8.6.1 Acknowledgments 232 References 232 9 Perceptual Hashing for Large-Scale Multimedia Search 239 LiWeng, I-Hong Jhuo and Wen-Huang Cheng 9.1 Introduction 240 9.1.1 Related work 240 9.1.2 Definitions and Properties of Perceptual Hashing 241 9.1.3 Multimedia Search using Perceptual Hashing 243 9.1.4 Applications of Perceptual Hashing 243 9.1.5 Evaluating Perceptual Hash Algorithms 244 9.2 Unsupervised Perceptual Hash Algorithms 245 9.2.1 Spectral Hashing 245 9.2.2 Iterative Quantization 246 9.2.3 K-Means Hashing 247 9.2.4 Kernelized Locality Sensitive Hashing 249 9.3 Supervised Perceptual Hash Algorithms 250 9.3.1 Semi-Supervised Hashing 250 9.3.2 Kernel-Based Supervised Hashing 252 9.3.3 Restricted Boltzmann Machine-Based Hashing 253 9.3.4 Supervised Semantic-Preserving Deep Hashing 255 9.4 Constructing Perceptual Hash Algorithms 257 9.4.1 Two-Step Hashing 257 9.4.2 Hash Bit Selection 258 9.5 Conclusion and Discussion 260 References 261 Part IV Applications of Large-Scale Multimedia Search 267 10 Image Tagging with Deep Learning: Fine-Grained Visual Analysis 269 Jianlong Fu and Tao Mei 10.1 Introduction 269 10.2 Basic Deep Learning Models 270 10.3 Deep Image Tagging for Fine-Grained Image Recognition 272 10.3.1 Attention Proposal Network 274 10.3.2 Classification and Ranking 275 10.3.3 Multi-Scale Joint Representation 276 10.3.4 Implementation Details 276 10.3.5 Experiments on CUB-200-2011 277 10.3.6 Experiments on Stanford Dogs 280 10.4 Deep Image Tagging for Fine-Grained Sentiment Analysis 281 10.4.1 Learning Deep Sentiment Representation 282 10.4.2 Sentiment Analysis 283 10.4.3 Experiments on SentiBank 283 10.5 Conclusion 284 References 285 11 Visually Exploring Millions of Images using Image Maps and Graphs 289 Kai Uwe Barthel and Nico Hezel 11.1 Introduction and Related Work 290 11.2 Algorithms for Image Sorting 293 11.2.1 Self-Organizing Maps 293 11.2.2 Self-Sorting Maps 294 11.2.3 Evolutionary Algorithms 295 11.3 Improving SOMs for Image Sorting 295 11.3.1 Reducing SOM Sorting Complexity 295 11.3.2 Improving SOM Projection Quality 297 11.3.3 Combining SOMs and SSMs 297 11.4 Quality Evaluation of Image Sorting Algorithms 298 11.4.1 Analysis of SOMs 298 11.4.2 Normalized Cross-Correlation 299 11.4.3 A New Image Sorting Quality Evaluation Scheme 299 11.5 2D Sorting Results 301 11.5.1 Image Test Sets 301 11.5.2 Experiments 302 11.6 Demo System for Navigating 2D Image Maps 304 11.7 Graph-Based Image Browsing 306 11.7.1 Generating Semantic Image Features 306 11.7.2 Building the Image Graph 307 11.7.3 Visualizing and Navigating the Graph 310 11.7.4 Prototype for Image Graph Navigation 312 11.8 Conclusion and Future Work 313 References 313 12 Medical Decision Support Using Increasingly Large Multimodal Data Sets 317 Henning Müller and Devrim Ünay 12.1 Introduction 317 12.2 Methodology for Reviewing the Literature in this chapter 320 12.3 Data, Ground Truth, and Scientific Challenges 321 12.3.1 Data Annotation and Ground Truthing 321 12.3.2 Scientific Challenges and Evaluation as a Service 321 12.3.3 Other Medical Data Resources Available 322 12.4 Techniques used for Multimodal Medical Decision Support 323 12.4.1 Visual and Non-Visual Features Describing the Image Content 323 12.4.2 General Machine Learning and Deep Learning 323 12.5 Application Types of Image-Based Decision Support 326 12.5.1 Localization 326 12.5.2 Segmentation 326 12.5.3 Classification 327 12.5.4 Prediction 327 12.5.5 Retrieval 327 12.5.6 Automatic Image Annotation 328 12.5.7 Other Application Types 328 12.6 Discussion on Multimodal Medical Decision Support 328 12.7 Outlook or the Next Steps of Multimodal Medical Decision Support 329 References 330 Conclusions and Future Trends 337 Index 339