OcrV1, Main, Exploration, bibRecord, 000011

A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification

Identifieur interne : 000011 ( Main/Exploration ); précédent : 000010; suivant : 000012

A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification

Auteurs : Cuihong Wen [République populaire de Chine] ; Jing Zhang [République populaire de Chine] ; Ana Rebelo [Portugal] ; Fanyong Cheng [République populaire de Chine]

Source :

PLoS ONE [ 1932-6203 ] ; 2016.

RBID : PMC:4795649

Abstract

Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs).

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4795649

DOI: 10.1371/journal.pone.0149688
PubMed: 26985826
PubMed Central: 4795649

Affiliations:

Portugal, République populaire de Chine

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A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification

A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification

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