Graph Based Multimedia Analysis (eBook)

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

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.

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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