Deep Learning in Mining of Visual Content (eBook)

Akka Zemmari, Jenny Benois-Pineau (Autoren)

eBook Download: PDF

2020 | 1st ed. 2020
XVII, 110 Seiten
Springer International Publishing (Verlag)
978-3-030-34376-7 (ISBN)

Infos

This book provides the reader with the fundamental knowledge in the area of deep learning with application to visual content mining. The authors give a fresh view on Deep learning approaches both from the point of view of image understanding and supervised machine learning. 

It contains chapters which introduce theoretical and mathematical foundations of neural networks and related optimization methods. Then it discusses some particular very popular architectures used in the domain: convolutional neural networks and recurrent neural networks. 

Deep Learning is currently at the heart of most cutting edge technologies. It is in the core of the recent advances in Artificial Intelligence. Visual information in Digital form is constantly growing in volume. In such active domains as Computer Vision and Robotics visual information understanding is based on the use of deep learning. Other chapters present applications of deep learning for visual content mining. These include attention mechanisms in deep neural networks and application to digital cultural content mining. An additional application field is also discussed, and illustrates how deep learning can be of very high interest to computer-aided diagnostics of Alzheimer's disease on multimodal imaging. 

This book targets advanced-level students studying computer science including computer vision, data analytics and multimedia. Researchers and professionals working in computer science, signal and image processing may also be interested in this book.

Akka Zemmari has received his Ph.D. degree from the University of Bordeaux 1, France, in 2000. He is an associate professor in computer science since 2001 at University of Bordeaux, France. His research interests include machine and deep learning, randomized algorithms and distributed algorithms and systems.

Jenny Benois-Pineau is a full professor of Computer science at the University Bordeaux and chair of Video Analysis and Indexing research group in Image and Sound Department of LABRI UMR 58000 Université Bordeaux/CNRS/IPB-ENSEIRB. She obtained her PhD degree in Signals and Systems in Moscou and her Habilitation à Diriger la Recherche in Computer Science and Image Processing from University of Nantes, France. Her topics of interest include image and video analysis and indexing, motion analysis and visual content interpretation with machine learning approaches. She is the author and co-author of more than 180 papers in international journals, conference proceedings, book chapters, co-editor of three books. She has tutored an co-tutored 26 PhD students. She is associated editor of EURASIP Signal Processing:Image Communication, Elsevier, Multimedia Tools and applications, Springer, and SPIE Journal of Electronic Imaging journals. She has served on numerous program committees of international conferences of IEEE, ACM and as an expert for international and national research bodies.  She is elected IEEE TC IVMSP member for the period of 2018-2020 and is Knight of Academic Palms Order.

Inhalt

Preface 7
Contents 9
Acronyms 12
List of Figures 13
1 Introduction 16
2 Supervised Learning Problem Formulation 19
2.1 Supervised Learning 19
2.2 Classification and Regression 20
2.3 Evaluation Metrics 21
2.3.1 Confusion Matrix 21
2.3.2 Metrics 23
2.3.3 AUC-ROC Curve 24
2.4 Conclusion 25
3 Neural Networks from Scratch 26
3.1 Formal Neuron 26
3.2 Artificial Neural Networks and Deep Neural Networks 29
3.3 Learning a Neural Network 30
3.3.1 Loss Function 31
3.3.2 One-Hot Encoding 31
3.3.3 Softmax or How to Transform Outputs into Probabilities 32
3.3.4 Cross-Entropy 32
3.4 Conclusion 33
4 Optimization Methods 34
4.1 Gradient Descent 34
4.2 Stochastic Gradient Descent 36
4.3 Momentum Based SGD 37
4.4 Nesterov Accelerated Gradient Descent 38
4.5 Adaptative Learning Rate 38
4.6 Extensions of Gradient Descent 40
4.7 Gradient Estimation in Neural Networks 41
4.7.1 A Simple Example 41
4.7.2 General Case: Backpropagation Algorithm 43
4.8 Conclusion 45
5 Deep in the Wild 47
5.1 Introduction 47
5.2 Convolution 48
5.3 Sub-sampling 54
5.3.1 Image Sampling 54
5.3.2 Sub-sampling of Images and Features 57
5.4 Conclusion 58
6 Convolutional Neural Networks as Image Analysis Tool 61
6.1 General Principles 61
6.2 Convolutional Layers 62
6.3 Max-Pooling Layers 64
6.4 Dropout 65
6.5 Some Well-Known CNNs Architectures 66
6.5.1 LeNet Architecture and MNIST Dataset 66
6.5.2 AlexNet Architecture 67
6.5.3 GoogLeNet 68
6.5.4 Other Important Architectures 68
6.6 Conclusion 70
7 Dynamic Content Mining 71
7.1 Hidden Markov Models 71
7.1.1 Likelihood Computation 73
7.1.2 Decoding: The Viterbi Algorithm 74
7.1.3 Learning an HMM 75
7.2 Recurrent Neural Networks 77
7.2.1 Definition 77
7.2.2 Training an RNN 79
7.3 Long-Short Term Memory Networks 80
7.3.1 The Model 80
7.4 Conclusion 81
8 Case Study for Digital Cultural Content Mining 82
8.1 Introduction 82
8.2 Visual Saliency 84
8.2.1 Top-Down Saliency Maps Built Upon Gaze Fixations 84
8.2.2 Co-saliency Propagation 86
8.3 Saliency in Deep CNNs 88
8.3.1 Attentional Mechanisms in Deep NNs 88
8.3.2 Squeeze-and-Excitation Networks 88
8.3.3 Double Attention Networks (A2-Nets) 88
8.3.4 Visual Saliency Propagation 90
8.3.4.1 Saliency in Pooling Layers 90
8.3.4.2 Saliency in the Dropout Layers 93
8.3.4.3 Use of Saliency in Backward Propagation 94
8.4 Conclusion 96
9 Introducing Domain Knowledge 97
9.1 Introduction 97
9.2 Domain Knowledge 98
9.2.1 Imaging Modalities 98
9.2.2 Selection of Brain ROI 100
9.2.3 Alignment of Different Imaging Modalities 101
9.3 Siamese Deep NNs for Fusion of Modalities 103
9.3.1 ``2D+?'' Approach for Classification of Brain Scans for AD Detection 104
9.3.2 Siamese Architecture 105
9.4 Conclusion 106
Conclusion 108
Glossary 109
References 110

Produkt-Details

• Erscheint lt. Verlag: 22.1.2020
• Reihe/Serie: SpringerBriefs in Computer Science
• Reihe/Serie: SpringerBriefs in Computer Science
• Zusatzinfo: XVII, 110 p. 46 illus., 25 illus. in color.
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Schlagworte: Artificial Intelligence • Artificial Neural Networks • computer vision • convolutional neural networks • Data Mining • Deep learning • Domain knowledge • Hidden Markov Chains • Long Short-Term Memory • Objects Recognition • Optimization Methods • Recurrent Neural Networks • Supervised Machine Learning • Visual Indexing
• ISBN-10: 3-030-34376-6 / 3030343766
• ISBN-13: 978-3-030-34376-7 / 9783030343767