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.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Theoretical background and related works - p. 5 3. Chapter 3 Real-time application of OPF-based classifier in Snort IDS - p. 55 4. Chapter 4 Optimum-path forest and active learning approaches for content-based medical image retrieval - p. 95 5. Chapter 5 Hybrid and modified OPFs for intrusion detection systems and large-scale problems - p. 109 6. Chapter 6 Detecting atherosclerotic plaque calcifications of the carotid artery through optimum-path forest - p. 137 7. Chapter 7 Learning to weight similarity measures with Siamese networks: a case study on optimum-path forest☆☆ The authors appreciate São Paulo Research Foundation (FAPESP) grants #2013/07375-0, #2014/12236-1, #2017/25908-6, #2018/15597-6, #2018/21934-5 and #2019/02205-5, and CNPq grants 307066/2017-7 and 427968/2018-6. - p. 155 8. Chapter 8 An iterative optimum-path forest framework for clustering - p. 175 9. Chapter 9 Future trends in optimum-path forest classification - p. 217 10. Index - p. 221
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.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Theoretical background and related works - p. 5 3. Chapter 3 Real-time application of OPF-based classifier in Snort IDS - p. 55 4. Chapter 4 Optimum-path forest and active learning approaches for content-based medical image retrieval - p. 95 5. Chapter 5 Hybrid and modified OPFs for intrusion detection systems and large-scale problems - p. 109 6. Chapter 6 Detecting atherosclerotic plaque calcifications of the carotid artery through optimum-path forest - p. 137 7. Chapter 7 Learning to weight similarity measures with Siamese networks: a case study on optimum-path forest☆☆ The authors appreciate São Paulo Research Foundation (FAPESP) grants #2013/07375-0, #2014/12236-1, #2017/25908-6, #2018/15597-6, #2018/21934-5 and #2019/02205-5, and CNPq grants 307066/2017-7 and 427968/2018-6. - p. 155 8. Chapter 8 An iterative optimum-path forest framework for clustering - p. 175 9. Chapter 9 Future trends in optimum-path forest classification - p. 217 10. Index - p. 221
