Optimum-Path Forest

Optimum-Path Forest

Theory, Algorithms, and Applications

1st Edition - January 6, 2022

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  • Editors: Alexandre Xavier Falcao, João Papa
  • Paperback ISBN: 9780128226889
  • eBook ISBN: 9780128226896

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Optimum-Path Forest: Theory, Algorithms, and Applications was first published in 2008 in its supervised and unsupervised versions with applications in medicine and image classification. Since then, it has expanded to a variety of other applications such as remote sensing, electrical and petroleum engineering, and biology. In recent years, multi-label and semi-supervised versions were also developed to handle video classification problems. The book presents the principles, algorithms and applications of Optimum-Path Forest, giving the theory and state-of-the-art as well as insights into future directions.

Key Features

  • Presents the first book on Optimum-path Forest
  • Shows how it can be used with Deep Learning
  • Gives a wide range of applications
  • Includes the methods, underlying theory and applications of Optimum-Path Forest (OPF)


Engineers and computer scientists working with machine and deep learning methods

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • Dedication
  • List of contributors
  • Biography of the editors
  • Alexandre Xavier Falcão
  • João Paulo Papa
  • Preface
  • Chapter 1: Introduction
  • Abstract
  • References
  • Chapter 2: Theoretical background and related works
  • Abstract
  • Acknowledgements
  • 2.1. Introduction
  • 2.2. The optimum-path forest framework
  • 2.3. Applications
  • 2.4. Conclusions and future trends
  • References
  • Chapter 3: Real-time application of OPF-based classifier in Snort IDS
  • Abstract
  • Acknowledgements
  • 3.1. Introduction
  • 3.2. Intrusion detection systems
  • 3.3. Machine learning
  • 3.4. Methodology
  • 3.5. Experiments and results
  • 3.6. Final considerations
  • References
  • Chapter 4: Optimum-path forest and active learning approaches for content-based medical image retrieval
  • Abstract
  • 4.1. Introduction
  • 4.2. Methodology
  • 4.3. Experiments
  • 4.4. Conclusion
  • 4.5. Funding and acknowledgments
  • References
  • Chapter 5: Hybrid and modified OPFs for intrusion detection systems and large-scale problems
  • Abstract
  • 5.1. Introduction
  • 5.2. Modified OPF-based IDS using unsupervised learning and social network concept
  • 5.3. Hybrid IDS using unsupervised OPF based on MapReduce approach
  • 5.4. Hybrid IDS using modified OPF and selected features
  • 5.5. Modified OPF using Markov cluster process algorithm
  • 5.6. Modified OPF based on coreset concept
  • 5.7. Enhancement of MOPF using k-medoids algorithm
  • References
  • Chapter 6: Detecting atherosclerotic plaque calcifications of the carotid artery through optimum-path forest
  • Abstract
  • 6.1. Introduction
  • 6.2. Theoretical background
  • 6.3. Methodology
  • 6.4. Experimental results
  • 6.5. Conclusions and future works
  • References
  • Chapter 7: Learning to weight similarity measures with Siamese networks: a case study on optimum-path forest
  • Abstract
  • 7.1. Introduction
  • 7.2. Theoretical background
  • 7.3. Methodology
  • 7.4. Experimental results
  • 7.5. Conclusion
  • References
  • Chapter 8: An iterative optimum-path forest framework for clustering
  • Abstract
  • Acknowledgements
  • 8.1. Introduction
  • 8.2. Related work
  • 8.3. The iterative optimum-path forest framework
  • 8.4. Experimental results
  • 8.5. Conclusions and future work
  • References
  • Chapter 9: Future trends in optimum-path forest classification
  • Abstract
  • References
  • Index

Product details

  • No. of pages: 244
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: January 6, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780128226889
  • eBook ISBN: 9780128226896

About the Editors

Alexandre Xavier Falcao

Alexandre Xavier Falcao is a full professor at the Institute of Computing (IC), University of Campinas (Unicamp), where he has worked since 1998. He attended the Federal University of Pernambuco from 1984-1988, where he got a B.Sc. in Electrical Engineering. He then attended Unicamp, where he got an M.Sc. (1993), and a Ph.D. (1996), in Electrical Engineering, by working on volumetric data visualization and medical image segmentation. During his Ph.D., he worked with the Medical Image Processing Group at the University of Pennsylvania from 1994-1996. In 2011-2012, he spent a one-year sabbatical at the Robert W. Holley Center for Agriculture and Health (USDA, Cornell University), working on image analysis applied to plant biology. He served as Associate Director of IC-Unicamp (2006-2007), Coordinator of its Post-Graduation Program (2009-2011), and Senior Area Editor of IEEE Signal Processing Letters (2016-2020). He is currently a top level research fellow at the for the Brazilian National Council for Scientific and Technological Development (CNPq), President of the Special Commission of Computer Graphics and Image Processing (CEGRAPI) for the Brazilian Computer Society (SBC), and Area Coordinator of Computer Science for the Sao Paulo Research Foundation (FAPESP). Among the several awards he received over the years, it is worth mentioning three Unicamp inventor awards at the category "License Technology" (2011, 2012, and 2020), three awards of academic excellence (2006, 2011, 2016) from IC-Unicamp, one award of academic recognition "Zeferino Vaz" from Unicamp (2014), and the best paper award in the year of 2012 from the journal Pattern Recognition (received at Stockholm, Sweden, during the conference ICPR 2014). His research work aims at computational models to learn and interpret the semantic content of images in the domain of several applications. The areas of interest include image and video processing, data visualization, medical image analysis, remote sensing, graph algorithms, image annotation, organization, and retrieval, and (interactive) machine learning and pattern recognition.

Affiliations and Expertise

Professor, Institute of Computing (IC), University of Campinas (Unicamp), Brazil

João Papa

João Papa
Joao Paulo Papa obtained his Ph.D. in Computer Science from University of Campinas, Brazil, in 2008, and was a visiting scholar at Harvard University from 2014-2015. He has been a Professor at Sao Paulo State University (UNESP), Brazil, since 2009, and his main interests include image processing, machine learning and meta-heuristic optimization.

Affiliations and Expertise

Assistant Professor, Sao Paulo State University (UNESP), Brazil; Visiting scholar, Harvard University, Cambridge, MA, USA

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