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Advances in Computer Vision and Pattern Recognition

Unsupervised Learning in Space and Time

A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks

Authors: Leordeanu, Marius

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  • Offers a novel approach to unsupervised learning, which connects seemingly disparate problems in the domain through unified mathematical formulations and efficient optimization algorithms
  • Explains, in a concise and detailed manner, how to solve specific and highly relevant tasks in computer vision and machine learning
  • Provides useful practical guidance and insights on unsupervised learning problems, in addition to a solid theoretical justification for each algorithm presented
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  • ISBN 978-3-030-42128-1
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About this book

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, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts.

Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way.

Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines. 


About the authors

Dr. Marius Leordeanu is an Associate Professor (Senior Lecturer) at the Computer Science & Engineering Department, Polytechnic University of Bucharest and a Senior Researcher at the Institute of Mathematics of the Romanian Academy (IMAR), Bucharest, Romania. In 2014, he was awarded the Grigore Moisil Prize, the most prestigious award in mathematics bestowed by the Romanian Academy, for his work on unsupervised learning.


Table of contents (8 chapters)

Table of contents (8 chapters)

Buy this book

eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-030-42128-1
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $139.99
price for USA in USD
  • ISBN 978-3-030-42127-4
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Unsupervised Learning in Space and Time
Book Subtitle
A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks
Authors
Series Title
Advances in Computer Vision and Pattern Recognition
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-42128-1
DOI
10.1007/978-3-030-42128-1
Hardcover ISBN
978-3-030-42127-4
Series ISSN
2191-6586
Edition Number
1
Number of Pages
XXIII, 298
Number of Illustrations
136 b/w illustrations, 96 illustrations in colour
Topics

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