Optimum-Path Forest based on k-connectivity: Theory and applications.

A deeper theoretical background about the Optimum-Path Forest (OPF) classifier with k-neighborhood (OPFk) is presented.A new, faster and less prone to error training step is also proposed.A faster classification algorithm for the OPFk classifier is presented.An extensive experimental evaluation is conducted.New insights about future research concerning OPFk are also discussed. Display Omitted Graph-based pattern recognition techniques have been in the spotlight for many years, since there is a constant need for faster and more effective approaches. Among them, the Optimum-Path Forest (OPF) framework has gained considerable attention in the last years, mainly due to the promising results obtained by OPF-based classifiers, which range from unsupervised, semi-supervised and supervised learning. In this paper, we consider a deeper theoretical explanation concerning the supervised OPF classifier with k-neighborhood (OPFk), as well as we proposed two different training and classification algorithms that allow OPFk to work faster. The experimental validation against standard OPF and Support Vector Machines also validates the robustness of OPFk in real and synthetic datasets.