Overview
- 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
Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)
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Table of contents (8 chapters)
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Front Matter
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Back Matter
Keywords
- Computer Vision
- Deep Learning
- Unsupervised Learning
- Applications of Convolutional Neural Networks
- Graph Matching
- Probabilistic Graphical Models
- Efficient Computational and Statistical Methods
- Fast Optimization Algorithms
- Semantic Segmentation in Video
- Object Discovery in Video
- Video Understanding and Analysis
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.
Authors and Affiliations
About the author
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.
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: Marius Leordeanu
Series Title: Advances in Computer Vision and Pattern Recognition
DOI: https://doi.org/10.1007/978-3-030-42128-1
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-42127-4Published: 18 April 2020
Softcover ISBN: 978-3-030-42130-4Published: 18 April 2021
eBook ISBN: 978-3-030-42128-1Published: 17 April 2020
Series ISSN: 2191-6586
Series E-ISSN: 2191-6594
Edition Number: 1
Number of Pages: XXIII, 298
Number of Illustrations: 136 b/w illustrations, 96 illustrations in colour
Topics: Image Processing and Computer Vision, Machine Learning, Mathematical Applications in Computer Science