Please use this identifier to cite or link to this item: http://hdl.handle.net/11328/2470
Title: A directed acyclic graph-large margin distribution machine model for music symbol classification
Authors: Wen, Cuihong
Zhang, Jing
Rebelo, Ana
Cheng, Fanyong
Keywords: Support vector machines
Bioacoustics
Algorithms
Kernel functions
Deformation
Hidden Markov models
Neural Networks
Issue Date: 17-Mar-2016
Publisher: Mansour Ebrahimi, Qom University, ISLAMIC REPUBLIC OF IRAN
Citation: 3. Wen, C., Zhang, J., Rebelo, A., & Cheng, F. (2016). A directed acyclic graph-large margin distribution machine model for music symbol classification. PLoS ONE, 11(3), 1-12. https://doi.org/10.1371/journal.pone.0149688. Disponível no Repositório UPT, http://hdl.handle.net/11328/2470
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).
URI: http://hdl.handle.net/11328/2470
Appears in Collections:INPP - Artigos em Revistas Internacionais / Papers in International Journals

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