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Unsupervised learning in space and time : a modern approach for computer vision using graph-based techniques and deep neural networks /

This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a foc...

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Bibliographic Details
Main Authors: Leordeanu, Marius
Published: Springer,
Publisher Address: Cham :
Publication Dates: [2020]
Literature type: Book
Language: English
Series: Advances in computer vision and pattern recognition,
Subjects:
Machine learning
Computer vision
Summary: This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field. Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, a
Carrier Form: xxiii, 298 pages : illustrations (some color), forms ; 24 cm.
Bibliography: Includes bibliographical references and index.
ISBN: 9783030421274 (hardback) :
3030421279 (hardback)
9783030421281 (electronic book)
3030421287 (electronic book)
Index Number: Q325
CLC: TP181
Call Number: TP181/L587
Contents: 1. Unsupervised Visual Learning: from Pixels to Seeing -- 2. Unsupervised Learning of Graph and Hypergraph Matching -- 3. Unsupervised Learning of Graph and Hypergraph Clustering -- 4. Feature Selection meets Unsupervised Learning -- 5. Unsupervised Learning of Object Segmentation in Video with Highly Probable Positive Features -- 6. Coupling Appearance and Motion: Unsupervised Clustering for Object Segmentation through Space and Time -- 7. Unsupervised Learning in Space and Time over Several Generations of Teacher and Student Networks -- 8. Unsupervised Learning Towards the Future.

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