Optical Music Recognition Based On Locality-constrained Linear Coding and Double Distribution Support Vector Machine

Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we adopt the descriptor of Locality-constrained Linear Coding (LLC) and Double Distribution Support Vector Machine (DDSVM) to address music symbol classification in OMR. LLC can obtain stronger feature representation ability compared with original music symbol. Recently, DDSVM is presented to obtain stronger robustness and generalization performance. Therefore, DDSVM is adopted to improve the classification performance in OMR. Moreover, One Versus Rest Double Distribution Support Vector Machine (OVR-DDSVM) is proposed for multi-class music symbol classification. OVR-DDSVM can obtain high accuracy only using linear kernel due to LLC descriptor, and this speeds the classification process. OVR-DDSVM is tested on more than 10000 music symbol images of 20 classes, and experimental results verify the superiority of LLC+DDSVM to other algorithms.