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

Author

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  • Cuihong Wen
  • Jing Zhang
  • Ana Rebelo
  • Fanyong Cheng

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).

Suggested Citation

  • Cuihong Wen & Jing Zhang & Ana Rebelo & Fanyong Cheng, 2016. "A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-11, March.
    • Handle: RePEc:plo:pone00:0149688
    DOI: 10.1371/journal.pone.0149688
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