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© 2020

Unsupervised Learning in Space and Time

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

Book

Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

About this book

Introduction

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.

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.

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

Authors and affiliations

  1. 1.Computer Science and Engineering DepartmentPolytechnic University of BucharestBucharestRomania

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.


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
  • Series Abbreviated Title Advs Comp. Vision, Pattern Recognition
  • DOI https://doi.org/10.1007/978-3-030-42128-1
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science Computer Science (R0)
  • Hardcover ISBN 978-3-030-42127-4
  • Softcover ISBN 978-3-030-42130-4
  • eBook ISBN 978-3-030-42128-1
  • 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
  • Buy this book on publisher's site
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