Optimum-Path Forest

Theory, Algorithms, and Applications

Häftad, Engelska, 2022

AvAlexandre Xavier Falcao,Jo�o Paulo Papa

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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.

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)

Produktinformation

Utgivningsdatum2022-01-24
Mått152 x 229 x 15 mm
Vikt410 g
FormatHäftad
SpråkEngelska
Antal sidor244
FörlagElsevier Science
ISBN9780128226889

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Alexandre Xavier Falcao is a full professor atthe Institute of Computing (IC), University of Campinas (Unicamp),where he has worked since 1998.He attended the Federal University of Pernambuco from 1984-1988, wherehe got a B.Sc. in Electrical Engineering. He then attended Unicamp, where he got an M.Sc. (1993), and a Ph.D. (1996), in ElectricalEngineering, by working on volumetric data visualization and medicalimage segmentation. During his Ph.D., he worked with the Medical ImageProcessing Group at the University of Pennsylvania from 1994-1996. In2011-2012, he spent a one-year sabbatical at the Robert W. HolleyCenter for Agriculture and Health (USDA, Cornell University), workingon image analysis applied to plant biology. He served as Associate Director of IC-Unicamp (2006-2007), Coordinatorof 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 forScientific and Technological Development (CNPq), President of theSpecial 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 inventorawards at the category "License Technology" (2011, 2012, and 2020), three awards of academic excellence (2006, 2011, 2016) fromIC-Unicamp, one award of academic recognition "Zeferino Vaz" fromUnicamp (2014), and the best paper award in the year of 2012 from the journal Pattern Recognition (received at Stockholm, Sweden, during theconference ICPR 2014).His research work aims at computational models to learn and interpretthe semantic content of images in the domain of several applications. The areas of interest include image and videoprocessing, data visualization, medical image analysis, remotesensing, graph algorithms, image annotation, organization, andretrieval, and (interactive) machine learning and pattern recognition. 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.

1. Introduction2. Theoretical Background and Related Works3. Real-time application of OPF-based classifier in Snort IDS4. Optimum-Path Forest and Active Learning Approaches for Content-Based Medical Image Retrieval5. Hybrid and Modified OPFs for Intrusion Detection Systems and Large-Scale Problems6. Detecting Atherosclerotic Plaque Calcifications of the Carotid Artery Through Optimum-Path Forest7. Learning to Weight Similarity Measures with Siamese Networks: A Case Study on Optimum-Path Forest8. An Iterative Optimum-Path Forest Framework for Clustering9. Future Trends in Optimum-Path Forest Classification