Hierarchical learning using deep optimum-path forest

•A new hierarchical approach to learn features based on the Optimum-Path Forest.•Results outperformed previous works from the research group.•Bag-of-Visual-Words is applied to aid computer-assisted Parkinson’s disease.•An extension of the original Deep Optimum-Path Forest has been proposed. Bag-of-V...

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Bibliographic Details
Published in:Journal of visual communication and image representation 2020-08, Vol.71, p.102823, Article 102823
Main Authors: Afonso, Luis C.S., Pereira, Clayton R., Weber, Silke A.T., Hook, Christian, Falcão, Alexandre X., Papa, João P.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Handwriting dynamics
Hierarchical representation
Optimum-path forest
Parkinson’s disease
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Summary:•A new hierarchical approach to learn features based on the Optimum-Path Forest.•Results outperformed previous works from the research group.•Bag-of-Visual-Words is applied to aid computer-assisted Parkinson’s disease.•An extension of the original Deep Optimum-Path Forest has been proposed. 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.
ISSN:1047-3203
1095-9076
DOI:10.1016/j.jvcir.2020.102823