1. Writer Identification in Old Music Manuscripts Using Contour-Hinge Feature and Dimensionality Reduction with an Autoencoder
- Author
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Yo Tomita, Lambertus Schomaker, Jean-Paul van Oosten, and Masahiro Niitsuma
- Subjects
business.industry ,Computer science ,Feature vector ,Dimensionality reduction ,Speech recognition ,Image processing ,Pattern recognition ,Autoencoder ,Identification (information) ,Range (mathematics) ,Feature (computer vision) ,Segmentation ,Artificial intelligence ,business - Abstract
Although most of the previous studies in writer identification in music scores assumed successful prior staff-line removal, this assumption does not hold when the music scores suffer from a certain level of degradation or deformation. The impact of staff-line removal on the result of writer identification in such documents is rather vague. In this study, we propose a novel writer identification method that requires no staff-line removal and no segmentation. Staff-line removal is virtually achieved without image processing, by dimensionality reduction with an autoencoder in Contour-Hinge feature space. The experimental result with a wide range of music manuscripts shows the proposed method can achieve favourable results without prior staff-line removal.
- Published
- 2013
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