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作者 Vrochidis, Stefanos
書名 Big Data Analytics for Large-Scale Multimedia Search
出版項 Newark : John Wiley & Sons, Incorporated, 2019
©2019
國際標準書號 9781119376989 (electronic bk.)
9781119376972
book jacket
說明 1 online resource (375 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
附註 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 -- 1.2.5 ResNet -- 1.2.5.1 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 -- 2.2 Video preprocessing and Machine Learning Essentials -- 2.2.1 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
2.4 Methods for Event Detection and Event‐Based Video Search -- 2.4.1 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 -- 3.2.1 Process Parallelization -- 3.2.2 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 -- 3.5.5 Process Parallelization -- 3.5.6 Results -- 3.6 Closing Remarks -- 3.7 Acknowledgements -- References -- Part II Learning Algorithms for Large-Scale Multimedia -- Chapter 4 Large‐Scale Video Understanding with Limited Training Labels -- 4.1 Introduction -- 4.2 Video Retrieval with Hashing -- 4.2.1 Overview -- 4.2.2 Unsupervised Multiple Feature Hashing -- 4.2.2.1 Framework -- 4.2.2.2 The Objective Function of MFH -- 4.2.2.3 Solution of MFH -- 4.2.2.3.1 Complexity Analysis -- 4.2.3 Submodular Video Hashing -- 4.2.3.1 Framework -- 4.2.3.2 Video Pooling -- 4.2.3.3 Submodular Video Hashing -- 4.2.4 Experiments
4.2.4.1 Experiment Settings -- 4.2.4.1.1 Video Datasets -- 4.2.4.1.2 Visual Features -- 4.2.4.1.3 Algorithms for Comparison -- 4.2.4.2 Results -- 4.2.4.2.1 CC_WEB_VIDEO -- 4.2.4.2.2 Combined Dataset -- 4.2.4.3 Evaluation of SVH -- 4.2.4.3.1 Results -- 4.3 Graph‐Based Model for Video Understanding -- 4.3.1 Overview -- 4.3.2 Optimized Graph Learning for Video Annotation -- 4.3.2.1 Framework -- 4.3.2.2 OGL -- 4.3.2.2.1 Terms and Notations -- 4.3.2.2.2 Optimal Graph-Based SSL -- 4.3.2.2.3 Iterative Optimization -- 4.3.3 Context Association Model for Action Recognition -- 4.3.3.1 Context Memory -- 4.3.4 Graph‐based Event Video Summarization -- 4.3.4.1 Framework -- 4.3.4.2 Temporal Alignment -- 4.3.5 TGIF: A New Dataset and Benchmark on Animated GIF Description -- 4.3.5.1 Data Collection -- 4.3.5.2 Data Annotation -- 4.3.6 Experiments -- 4.3.6.1 Experimental Settings -- 4.3.6.2 Results -- 4.4 Conclusions and Future Work -- References -- Chapter 5 Multimodal Fusion of Big Multimedia Data -- 5.1 Multimodal Fusion in Multimedia Retrieval -- 5.1.1 Unsupervised Fusion in Multimedia Retrieval -- 5.1.1.1 Linear and Non‐linear Similarity Fusion -- 5.1.1.2 Cross‐modal Fusion of Similarities -- 5.1.1.3 Random Walks and Graph‐based Fusion -- 5.1.1.4 A Unifying Graph‐based Model -- 5.1.2 Partial Least Squares Regression -- 5.1.3 Experimental Comparison -- 5.1.3.1 Dataset Description -- 5.1.3.2 Settings -- 5.1.3.3 Results -- 5.1.4 Late Fusion of Multiple Multimedia Rankings -- 5.1.4.1 Score Fusion -- 5.1.4.2 Rank Fusion -- 5.1.4.2.1 Borda Count Fusion -- 5.1.4.2.2 Reciprocal Rank Fusion -- 5.1.4.2.3 Condorcet Fusion -- 5.2 Multimodal Fusion in Multimedia Classification -- 5.2.1 Related Literature -- 5.2.2 Problem Formulation -- 5.2.3 Probabilistic Fusion in Active Learning -- 5.2.3.1 If P(S&equals -- 0 ,T)≠0: -- 5.2.3.2 If P(S&equals -- 0 ,T)&equals -- 0:
5.2.3.3 Incorporating Informativeness in the Selection (P(S )) -- 5.2.3.4 Measuring Oracle's Confidence (P(S )) -- 5.2.3.5 Re‐training -- 5.2.4 Experimental Comparison -- 5.2.4.1 Datasets -- 5.2.4.2 Settings -- 5.2.4.3 Results -- 5.2.4.3.1 Expanding with Positive, Negative or Both -- 5.2.4.3.2 Comparing with Sample Selection Approaches -- 5.2.4.3.3 Comparing with Fusion Approaches -- 5.2.4.3.4 Parameter Sensitivity Investigation -- 5.2.4.3.5 Comparing with Existing Methods -- 5.3 Conclusions -- References -- Chapter 6 Large‐Scale Social Multimedia Analysis -- 6.1 Social Multimedia in Social Media Streams -- 6.1.1 Social Multimedia -- 6.1.2 Social Multimedia Streams -- 6.1.3 Analysis of the Twitter Firehose -- 6.1.3.1 Dataset: Overview -- 6.1.3.2 Linked Resource Analysis -- 6.1.3.3 Image Content Analysis -- 6.1.3.4 Geographic Analysis -- 6.1.3.5 Textual Analysis -- 6.2 Large‐Scale Analysis of Social Multimedia -- 6.2.1 Large‐Scale Processing of Social Multimedia Analysis -- 6.2.1.1 Batch‐Processing Frameworks -- 6.2.1.2 Stream‐Processing Frameworks -- 6.2.1.3 Distributed Processing Frameworks -- 6.2.2 Analysis of Social Multimedia -- 6.2.2.1 Analysis of Visual Content -- 6.2.2.2 Analysis of Textual Content -- 6.2.2.3 Analysis of Geographical Content -- 6.2.2.4 Analysis of User Content -- 6.3 Large‐Scale Multimedia Opinion Mining System -- 6.3.1 System Overview -- 6.3.2 Implementation Details -- 6.3.2.1 Social Media Data Crawler -- 6.3.2.2 Social Multimedia Analysis -- 6.3.2.3 Analysis of Visual Content -- 6.3.3 Evaluations: Analysis of Visual Content -- 6.3.3.1 Filtering of Synthetic Images -- 6.3.3.2 Near‐Duplicate Detection -- 6.4 Conclusion -- References -- Chapter 7 Privacy and Audiovisual Content: Protecting Users as Big Multimedia Data Grows Bigger -- 7.1 Introduction -- 7.1.1 The Dark Side of Big Multimedia Data
7.1.2 Defining Multimedia Privacy -- 7.2 Protecting User Privacy -- 7.2.1 What to Protect -- 7.2.2 How to Protect -- 7.2.3 Threat Models -- 7.3 Multimedia Privacy -- 7.3.1 Privacy and Multimedia Big Data -- 7.3.2 Privacy Threats of Multimedia Data -- 7.3.2.1 Audio Data -- 7.3.2.2 Visual Data -- 7.3.2.3 Multimodal Threats -- 7.4 Privacy‐Related Multimedia Analysis Research -- 7.4.1 Multimedia Analysis Filters -- 7.4.2 Multimedia Content Masking -- 7.5 The Larger Research Picture -- 7.5.1 Multimedia Security and Trust -- 7.5.2 Data Privacy -- 7.6 Outlook on Multimedia Privacy Challenges -- 7.6.1 Research Challenges -- 7.6.1.1 Multimedia Analysis -- 7.6.1.2 Data -- 7.6.1.3 Users -- 7.6.2 Research Reorientation -- 7.6.2.1 Professional Paranoia -- 7.6.2.2 Privacy as a Priority -- 7.6.2.3 Privacy in Parallel -- References -- Part III Scalability in Multimedia Access -- Chapter 8 Data Storage and Management for Big Multimedia -- 8.1 Introduction -- 8.1.1 Multimedia Applications and Scale -- 8.1.2 Big Data Management -- 8.1.3 System Architecture Outline -- 8.1.4 Metadata Storage Architecture -- 8.1.4.1 Lambda Architecture -- 8.1.4.2 Storage Layer -- 8.1.4.3 Processing Layer -- 8.1.4.4 Serving Layer -- 8.1.4.5 Dynamic Data -- 8.1.5 Summary and Chapter Outline -- 8.2 Media Storage -- 8.2.1 Storage Hierarchy -- 8.2.1.1 Secondary Storage -- 8.2.1.2 The Five‐Minute Rule -- 8.2.1.3 Emerging Trends for Local Storage -- 8.2.2 Distributed Storage -- 8.2.2.1 Distributed Hash Tables -- 8.2.2.2 The CAP Theorem and the PACELC Formulation -- 8.2.2.3 The Hadoop Distributed File System -- 8.2.2.4 Ceph -- 8.2.3 Discussion -- 8.3 Processing Media -- 8.3.1 Metadata Extraction -- 8.3.2 Batch Processing -- 8.3.2.1 Map‐Reduce and Hadoop -- 8.3.2.2 Spark -- 8.3.2.3 Comparison -- 8.3.3 Stream Processing -- 8.4 Multimedia Delivery -- 8.4.1 Distributed In‐Memory Buffering
8.4.1.1 Memcached and Redis
Description based on publisher supplied metadata and other sources
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
鏈接 Print version: Vrochidis, Stefanos Big Data Analytics for Large-Scale Multimedia Search Newark : John Wiley & Sons, Incorporated,c2019 9781119376972
主題 Multimedia data mining.
Big data
Electronic books
Alt Author Huet, Benoit
Chang, Edward Y
Kompatsiaris, Ioannis
Huet, Benoit
記錄 1 之 3
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