Music Document Layout Analysis through Machine Learning and Human Feedback

被引：10

作者：

Calvo-Zaragoza, Jorge ^{[2
]}

Zhang, Ke ^{[1
]}

Saleh, Zeyad ^{[1
]}

Vigliensoni, Gabriel ^{[2
]}

Fujinaga, Ichiro ^{[2
]}

机构：

[1] McGill Univ, Sch Elect & Comp Engn, Montreal, PQ, Canada

[2] McGill Univ, Schulich Sch Mus, Montreal, PQ, Canada

来源：

2017 14TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR 2017), VOL 2 | 2017年

关键词：

Terms Music Document Layout Analysis; Optical Music Recognition; Machine Learning; Human-aided computing;

D O I：

10.1109/ICDAR.2017.259

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Music documents often include musical symbols as well as other relevant elements such as staff lines, text, and decorations. To detect and separate these constituent elements, we propose a layout analysis framework based on machine learning that focuses on pixel-level classification of the image. For that, we make use of supervised learning classifiers trained to infer the category of each pixel. In addition, our scenario considers a human-aided computing approach in which the user is part of the recognition loop, providing feedback where relevant errors are made.

引用

页码：23 / 24

页数：2

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