Multi-Forest Classification and Layered Exhaustive Search using a Fully Hierarchical Hand Posture/Gesture Database

Amin Dadgar, Guido Brunnett

2018

Abstract

In this paper, we propose a systematic approach to building an entirely hierarchical hand posture database. The hierarchy provides the possibility of considering a large number of hand poses while requires a low time-space complexity for construction. Furthermore, two algorithms (random decision forest and exhaustive search) are chosen and tested on this database. We show that by utilizing such a database one will achieve better performances on classifiers’ training and search strategies (two main categories of the algorithms in the field of machine learning) compared with conventional (all-in-one-layer) databases.

Paper Citation

in Harvard Style

Dadgar A. and Brunnett G. (2018). Multi-Forest Classification and Layered Exhaustive Search using a Fully Hierarchical Hand Posture/Gesture Database. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP; ISBN 978-989-758-290-5, SciTePress, pages 121-128. DOI: 10.5220/0006591601210128

in Bibtex Style

@conference{visapp18,
author={Amin Dadgar and Guido Brunnett},
title={Multi-Forest Classification and Layered Exhaustive Search using a Fully Hierarchical Hand Posture/Gesture Database},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP},
year={2018},
pages={121-128},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006591601210128},
isbn={978-989-758-290-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP
TI - Multi-Forest Classification and Layered Exhaustive Search using a Fully Hierarchical Hand Posture/Gesture Database
SN - 978-989-758-290-5
AU - Dadgar A.
AU - Brunnett G.
PY - 2018
SP - 121
EP - 128
DO - 10.5220/0006591601210128
PB - SciTePress