TY - JOUR A1 - Afonso, Luis Claudio Sugi A1 - Pereira, Clayton R. A1 - Weber, Silke A. T. A1 - Hook, Christian A1 - Falcão, Alexandre X. A1 - Papa, Joao Paulo T1 - Hierarchical learning using deep optimum-path forest JF - Journal of Visual Communication and Image Representation N2 - Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used in several domains, which include computer-assisted medical diagnoses. In this work, we are interested in developing tools for the automatic identification of Parkinson’s disease using machine learning and the concept of BoVW. The proposed approach concerns a hierarchical-based learning technique to design visual dictionaries through the Deep Optimum-Path Forest classifier. The proposed method was evaluated in six datasets derived from data collected from individuals when performing handwriting exams. Experimental results showed the potential of the technique, with robust achievements. KW - Handwriting dynamics KW - Hierarchical representation KW - Optimum-path forest KW - Parkinson’s disease Y1 - 2020 U6 - https://doi.org/10.1016/j.jvcir.2020.102823 VL - 71 IS - August PB - Elsevier ER -