Skip to main content
Elsevier
View Cart

Optimum-Path Forest

Theory, Algorithms, and Applications

  • 1st Edition - January 6, 2022
  • Latest edition
  • Editors: Alexandre Xavier Falcao, João Paulo Papa
  • Language: English

The Optimum-Path Forest (OPF) classifier was first published in 2008 in its supervised and unsupervised versions with applications in medicine and image classification. Since t… Read more

World Book Day celebration

Where learning shapes lives

Up to 25% off trusted resources that support research, study, and discovery.

Explore resources

Description

The Optimum-Path Forest (OPF) classifier 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)

Readership

Engineers and computer scientists working with machine and deep learning methods

Table of contents

1. Introduction

2. Theoretical Background and Related Works

3. Real-time application of OPF-based classifier in Snort IDS

4. Optimum-Path Forest and Active Learning Approaches for Content-Based Medical Image Retrieval

5. Hybrid and Modified OPFs for Intrusion Detection Systems and Large-Scale Problems

6. Detecting Atherosclerotic Plaque Calcifications of the Carotid Artery Through Optimum-Path Forest

7. Learning to Weight Similarity Measures with Siamese Networks: A Case Study on Optimum-Path Forest

8. An Iterative Optimum-Path Forest Framework for Clustering

9. Future Trends in Optimum-Path Forest Classification

Product details

  • Edition: 1
  • Latest edition
  • Published: January 24, 2022
  • Language: English

About the editors

AX

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

JP

João Paulo 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

View book on ScienceDirect

Read Optimum-Path Forest on ScienceDirect