基于稀疏编码空间金字塔匹配的高中统计图形识别
发布时间：2018-11-07 06:48
【摘要】：随着人工智能技术的飞速发展,人工智能+教育将开启教育新模式,将人工智能的最新研究成果应用于教育是目前国际研究热点和难点之一。在人工智能+教育领域中类人解题是目前兴起的主要研究方向之一。类人解题对于人工智能+教育的实现具有重要的理论和实用价值。本论文主要针对类人解题研究中的高中统计图形识别问题进行了研究。本论文的主要工作如下:(1)构建统计图形数据库。收集四种典型的统计图形,构建了高中统计图形数据库,为后续的研究奠定基础。(2)设计了统计图形分类方法。利用稀疏编码空间金字塔匹配算法提取高中统计图形特征进行分类,该方法可以获取图形的本质特征,有利于得到正确分类。实验结果表明,利用稀疏编码空间金字塔匹配算法提取的图形特征较稳定,得到了较高的分类精度。(3)典型统计图形题意信息识别。在正确分类基础之上,本文针对直方图的特点,设计了直方图识别算法,识别直方图中的题意信息,并初步验证了其有效性。
[Abstract]:With the rapid development of artificial intelligence technology, artificial intelligence education will open a new model of education, and it is one of the hot and difficult points to apply the latest research results of artificial intelligence to education. Human-like problem solving is one of the main research directions in the field of artificial intelligence education. Human-like problem solving has important theoretical and practical value for the realization of artificial intelligence education. This paper mainly focuses on the problem of high school statistical graph recognition in the research of human-like problem solving. The main work of this paper is as follows: (1) build a statistical graphics database. Four typical statistical graphs are collected and the database of high school statistical graphics is constructed to lay the foundation for further research. (2) the classification method of statistical graphics is designed. The sparse coding space pyramid matching algorithm is used to extract the feature of high school statistical graph for classification. This method can obtain the essential feature of the graph and is helpful to get the correct classification. The experimental results show that the image features extracted by the sparse coding space pyramid matching algorithm are stable and the classification accuracy is high. (3) the typical statistical graph subject information recognition. According to the characteristics of histogram, a histogram recognition algorithm is designed to identify the title information in histogram, and its validity is preliminarily verified.
【学位授予单位】：华中师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：G434
本文编号：2315535
[Abstract]:With the rapid development of artificial intelligence technology, artificial intelligence education will open a new model of education, and it is one of the hot and difficult points to apply the latest research results of artificial intelligence to education. Human-like problem solving is one of the main research directions in the field of artificial intelligence education. Human-like problem solving has important theoretical and practical value for the realization of artificial intelligence education. This paper mainly focuses on the problem of high school statistical graph recognition in the research of human-like problem solving. The main work of this paper is as follows: (1) build a statistical graphics database. Four typical statistical graphs are collected and the database of high school statistical graphics is constructed to lay the foundation for further research. (2) the classification method of statistical graphics is designed. The sparse coding space pyramid matching algorithm is used to extract the feature of high school statistical graph for classification. This method can obtain the essential feature of the graph and is helpful to get the correct classification. The experimental results show that the image features extracted by the sparse coding space pyramid matching algorithm are stable and the classification accuracy is high. (3) the typical statistical graph subject information recognition. According to the characteristics of histogram, a histogram recognition algorithm is designed to identify the title information in histogram, and its validity is preliminarily verified.
【学位授予单位】：华中师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：G434
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