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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 multi...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ, USA :
Wiley,
[2018]
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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 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
- 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
- 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
- 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