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Visual music score detection with unsupervised feature learning method based on K-means

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Abstract

Automatic music score detection plays important role in the optical music recognition (OMR). In a visual image, the characteristic of the music scores is frequently degraded by illumination, distortion and other background elements. In this paper, to reduce the influences to OMR caused by those degradations especially the interference of Chinese character, an unsupervised feature learning detection method is proposed for improving the correctness of music score detection. Firstly, a detection framework was constructed. Then sub-image block features were extracted by simple unsupervised feature learning (UFL) method based on K-means and classified by SVM. Finally, music score detection processing was completed by connecting component searching algorithm based on the sub-image block label. Taking Chinese text as the main interferences, the detection rate was compared between UFL method and texture feature method based on 2D Gabor filter in the same framework. The experiment results show that unsupervised feature learning method gets less error detection rate than Gabor texture feature method with limited training set.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61375075) and Natural Science Foundation of Hebei Province (No. F2011201106, No. F2012201020).

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Agricultural University of Hebei, Lingyusi Street, No. 289, Baoding, China

    Yang Fang & Teng Gui-fa

  2. College of Mathematics and Computer Science, Hebei University, Wusi East Road, No. 180, Baoding, China

    Yang Fang

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  1. Yang Fang
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  2. Teng Gui-fa
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Correspondence to Teng Gui-fa.

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Fang, Y., Gui-fa, T. Visual music score detection with unsupervised feature learning method based on K-means. Int. J. Mach. Learn. & Cyber. 6, 277–287 (2015). https://doi.org/10.1007/s13042-014-0260-2

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  • DOI: https://doi.org/10.1007/s13042-014-0260-2

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