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 Название: Graph Based Multimedia Analysis
Автор: Ananda S. Chowdhury, Abhimanyu Sahu
Издательство: Morgan Kaufmann/Elsevier
Год: 2024
Страниц: 370
Язык: английский
Формат: epub
Размер: 72.1 MB
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
This book provides a comprehensive and methodical approach to these challenges, underpinned by the rigorous and elegant framework of graph theory. It deftly bridges the gap between the somewhat disparate domains of video processing and graph theory, demonstrating how graph-based methodologies can effectively address critical problems in video summarization, cosummarization, and action recognition.
The salient feature of this work is its extensive utilization of graph theoretical concepts. The book covers an impressive array of graph-based methods, including Delaunay graphs, Optimal Path-Forests, Bipartite Graph Matching, Graph Centrality measures, Graph Connectedness, Spectral Measures of Graph Similarity, Minimum Spanning Tree, and Random Walks. The integration of game-theoretic models, constrained optimization techniques, and advanced Deep Learning methods such as Convolutional Neural Networks and Transfer Learning, further enriches the suite of methods presented.
The book is well structured, beginning with an introductory chapter that lays the groundwork for the subsequent, more detailed explorations. The second chapter provides the theoretical foundations essential for understanding the advanced topics discussed later. Chapters three and four delve into exocentric video summarization, with a focus on mono-view summarization and multi-view setups, respectively. The next three chapters shift focus to egocentric video, addressing summarization, cosummarization, and action recognition in this context. The final chapter offers a synthesis of the findings and outlines plausible future research directions. An appendix is included, providing detailed implementation insights, making the theoretical discussions accessible for practical application.
This book signifies an important contribution to the field of multimedia analysis. It exemplifies the impact of mixed approaches, where theoretical insights from graph theory are harnessed to solve complex, real-world problems in video processing. The authors' expertise and unique perspective are evident throughout, making this work a valuable resource for researchers, practitioners, and scholars seeking to advance their understanding in this domain.
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Автор: Ingvar16 10-08-2024, 13:08 | Напечатать |
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Graph-Based Social Media Analysis Название: Graph-Based Social Media Analysis Автор: Ioannis Pitas Издательство: CRC Press Год: 2015 Формат: PDF Размер: 25 Мб Язык: английский /... |
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