Graph Based Multimedia Analysis - Softcover

S Chowdhury; Sahu

Softcover

ISBN 10: 0443214956 ISBN 13: 9780443214950

Publisher: Morgan Kaufmann, 2024

Synopsis

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.

• Addresses a number of challenging state-of-the-art problems in multimedia analysis like summarization, co-summarization, and action recognition
• Handles a wide class of video with different genres, durations, and numbers
• Applies a class of theoretically rich algorithms from the discipline of graph theory, in conjunction with deep learning, constrained optimization and game theory
• Includes thorough complexity analyses of the proposed solutions, and an appendix containing implementable source codes

About this title

About the Authors

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.

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.

From the Back Cover

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.

Edition details

• Publisher: Morgan Kaufmann
• Publication date: 2024
• ISBN 10: 0443214956
• ISBN 13: 9780443214950
• Binding: Paperback
• Language: English
• Number of pages: 366