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.

A comprehensive overview of winning methods in visual content mining Illustration of main concepts with graphical examples Tracing analogy with classical visual content analysis tools

Autorentext

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
Introduction.- Supervised Learning Problem Formulation.- Neural Networks from Scratch.- Optimization Methods.- Deep in the Wild.- Convolutional Neural Networks as Image Analysis Tool.- Dynamic Content Mining.- Case Study for Digital Cultural Content Mining.- Introducing Domain Knowledge. <p