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High School Statistical Graph Classification Using Hierarchical Model for Intelligent Mathematics Problem Solving

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Book cover Image and Video Technology (PSIVT 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10799))

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Abstract

High school statistical graph classification is one of the key steps in intelligent mathematics problem solving system. In this paper, a hierarchial classification method is proposed for high school statistical graph classification. Firstly, the dense Scale-invariant Feature Transform (SIFT) features of the input images are extracted. Secondly, the sparse coding of the SIFT features are obtained. Thirdly, these sparse features are pooled in multiscale. Finally, these pooled features are concatenated and then fed into single-hidden layer feedforward neural network for classification. The effectiveness of the proposed method is demonstrated on the constructed dataset, which contains 400 statistical graphs. In contrast to several state-of-the-art methods, the proposed method achieves better performance in terms of classification accuracy, especially when the size of the training samples is small.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grants 61502195 and 61772012, in part by the National Science & Technology Supporting Program during the Twelfth Five-year Plan Period granted by the Ministry of Science and Technology of China under Grant 2015BAK27B02, in part by the Humanities and Social Science project of Chinese Ministry of Education under Grant 17YJA880104, and in part by the Self-Determined Research Funds of CCNU From the Colleges’ Basic Research and Operation of MOE under Grants CCNU16A05022 and CCNU15A02020.

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Authors and Affiliations

  1. School of Educational Information Technology, Central China Normal University, Wuhan, 430079, China

    Yantao Wei, Yafei Shi, Huang Yao, Gang Zhao & Qingtang Liu

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  1. Yantao Wei
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  2. Yafei Shi
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  3. Huang Yao
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  4. Gang Zhao
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  5. Qingtang Liu
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Correspondence to Yantao Wei .

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Editors and Affiliations

  1. National Institute of Informatics, Tokyo, Japan

    Shin'ichi Satoh

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Wei, Y., Shi, Y., Yao, H., Zhao, G., Liu, Q. (2018). High School Statistical Graph Classification Using Hierarchical Model for Intelligent Mathematics Problem Solving. In: Satoh, S. (eds) Image and Video Technology. PSIVT 2017. Lecture Notes in Computer Science(), vol 10799. Springer, Cham. https://doi.org/10.1007/978-3-319-92753-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-92753-4_8

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  • Print ISBN: 978-3-319-92752-7

  • Online ISBN: 978-3-319-92753-4

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