title: A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.
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).
authors.affiliation: College of electrical and information engineering, Hunan University, Changsha, China.
authors.forename: Cuihong
authors.initials: C
authors.lastname: Wen
authors.affiliation: College of electrical and information engineering, Hunan University, Changsha, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.affiliation: INESC Porto, Universidade do Porto, Porto, Portugal.
authors.forename: Ana
authors.initials: A
authors.lastname: Rebelo
authors.affiliation: College of electrical and information engineering, Hunan University, Changsha, China.
authors.forename: Fanyong
authors.initials: F
authors.lastname: Cheng
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D000069550:Machine Learning; D009146:Music; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D060388:Support Vector Machine