Graph Based Multimedia Analysis

Erscheinungsjahr 2024
ISBN: 978-0-443-21495-0
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 152 mm x 229 mm, Gewicht: 600 g

Graph Based Multimedia Analysis applies concepts from graph theory to the problems of analyzing overabundant video data. Video data can be quite diverse: exocentric (captured by a standard camera) or egocentric (captured by a wearable device like Google Glass); of various durations (ranging from a few seconds to several hours); and could be from a single source or multiple sources. Efficient extraction of important information from such a large class of diverse video data can be overwhelming. The book, with its rich repertoire of theoretically elegant solutions, from graph theory in conjunction with deep learning, constrained optimization, and game theory, empowers the audience to achieve tasks like obtaining concise yet useful summaries and precisely recognizing single as well as multiple actions in a computationally efficient manner. The book provides a unique treatise on topics like egocentric video analysis and scalable video processing.

Autoren/Hrsg.

S Chowdhury, Ananda

Sahu, Abhimanyu

Fachgebiete

• Mathematik | Informatik Mathematik Operations Research Graphentheorie
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Multimedia
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

1. Introduction
2. Theoretical Foundations
3. Exocentric Video Summarization
4. Multi-view Exocentric Video Summarization
5. Egocentric Video Summarization
6. Egocentric Video Co-summarization
7. Action Recognition in Egocentric Video

Über Autor(innen)

Sahu, Abhimanyu
Abhimanyu Sahu is an Assistant Professor in the Department of Computer Science & Engineering at Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India. He received his Ph.D. degree in Computer Engineering from Jadavpur University, Kolkata, India in 2021. He is a life member of ISTE. His current research interests include computer vision, multimedia analysis, and pattern recognition problems. Specifically, he is also interested in exploring different machine learning techniques (self-supervised/unsupervised learning, deep Learning) to solve several challenging problems in multimedia analysis such as summarization and action/object/activity recognition particularly in first-person (Egocentric) videos. He also worked on theoretical aspects of Graph-based modeling of the above fields.

S Chowdhury, Ananda
Ananda S. Chowdhury is a Professor and former Head in the Department of Electronics and Telecommunication Engineering at Jadavpur University, Kolkata, India, where he leads the Imaging, Vision and Pattern Recognition group. He received his Ph.D. degree in Computer Science from The University of Georgia, Athens, GA, USA, and was a Postdoctoral Fellow at National Institutes of Health, Bethesda, MD, USA. His research interests include computer vision, pattern recognition, biomedical image/ signal processing, and multimedia analysis. He is a Senior Member of IEEE, a Member of the International Association for Pattern Recognition Technical Committee (IAPR TC) on Graph based Representations (GbR), and a life member of the Indian Unit for Pattern Recognition and Artificial Intelligence (IUPRAI). He has held invited academic visits to different universities across France, Germany, Norway, Italy, The Netherlands, Singapore and Brazil. Dr. Chowdhury serves/has served on the editorial boards of IEEE Transactions on Image Processing, Pattern Recognition Letters, IEEE Signal Processing Letters, and Springer Nature Computer Science. His Erdös Number is two.