Optimum-Path Forest

Theory, Algorithms, and Applications
Erscheinungsjahr 2022
ISBN: 978-0-12-822688-9
Verlag: William Andrew Publishing

Buch, Englisch, 244 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 410 g

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.

Autoren/Hrsg.

Xavier Falcao, Alexandre

Papa, João Paulo

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

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

Über Autor(innen)

Xavier Falcao, Alexandre
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.

Papa, João Paulo
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.