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.
机构署名：
本校为第一且通讯机构
院系归属：
教育信息技术学院
摘要：
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 effect...
