loading
Papers

Research.Publish.Connect.

Paper

Authors: Jorge Calvo-Zaragoza ; Jose J. Valero-Mas and Juan R. Rico-Juan

Affiliation: University of Alicante, Spain

ISBN: 978-989-758-222-6

Keyword(s): Optical Music Recognition, Music Symbols, Handwriting Recognition, Symbol Classification, Meta-features Space.

Related Ontology Subjects/Areas/Topics: Classification ; Ensemble Methods ; Multiclassifier Fusion ; Pattern Recognition ; Theory and Methods

Abstract: The classification of musical symbols is an important step for Optical Music Recognition systems. However, little progress has been made so far in the recognition of handwritten notation. This paper considers a scheme that combines ideas from ensemble classifiers and dissimilarity space to improve the classification of handwritten musical symbols. Several sets of features are extracted from the input. Instead of combining them, each set of features is used to train a weak classifier that gives a confidence for each possible category of the task based on distance-based probability estimation. These confidences are not combined directly but used to build a new set of features called Confidence Matrix, which eventually feeds a final classifier. Our work demonstrates that using this set of features as input to the classifiers significantly improves the classification results of handwritten music symbols with respect to other features directly retrieved from the image.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.214.184.124

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Calvo-Zaragoza, J.; J. Valero-Mas, J. and R. Rico-Juan, J. (2017). Recognition of Handwritten Music Symbols using Meta-features Obtained from Weak Classifiers based on Nearest Neighbor.In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 96-104. DOI: 10.5220/0006120200960104

@conference{icpram17,
author={Jorge Calvo{-}Zaragoza. and Jose J. Valero{-}Mas. and Juan R. Rico{-}Juan.},
title={Recognition of Handwritten Music Symbols using Meta-features Obtained from Weak Classifiers based on Nearest Neighbor},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={96-104},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006120200960104},
isbn={978-989-758-222-6},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Recognition of Handwritten Music Symbols using Meta-features Obtained from Weak Classifiers based on Nearest Neighbor
SN - 978-989-758-222-6
AU - Calvo-Zaragoza, J.
AU - J. Valero-Mas, J.
AU - R. Rico-Juan, J.
PY - 2017
SP - 96
EP - 104
DO - 10.5220/0006120200960104

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.