智能人机交互中的图形识别技术
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摘要
本文对智能人机交互中的图形识别方法进行了深入系统地研究，在研究中，应用了近年来模式识别中分类器联合方面以及图像匹配方面的一些新方法，并且结合应用背景对这些方法进行了进一步的改进。本文围绕智能人机交互中的图形识别这个主题，分别讨论了联机图形笔划的单分类器识别、联机图形笔划的多分类器联合识别、联机图形的结构识别、脱机图形符号识别和图形符号语义联想等内容。
第一章，介绍了人机交互的系统模型，以及人机交互中的图形识别技术。回顾了人机交互技术的历史与现状。接着介绍了本文的研究成果和本文的章节安排。
第二章，选择了三种不同分类器对联机图形单笔划进行分类。每一种分类器都基于互不相同的特征集合和不同的分类机制，具有较强的独立性。使用的分类器分别为基于模糊特征的线性分类器、使用几何定义特征的分层分类器以及基于曲度频域特征的距离分类器。对三种分类器的性能进行了实验比较，实验结果说明几种分类器具有一定的互补性。
第三章，研究了基于不同模式特征的多分类器联合方法问题。基于一个理论框架，从理论上提出了一种新的分类器联合方法，并对该联合方法进行了性能估计。将这种方法应用于联机几何图形识别问题，联合了三种分类器，每种分类器的机制各不相同，并且都基于不同的模式特征。并且将这种分类器联合方法与几种现有的联合方法进行了实验比较，实验结果说明这种方法具有较高的识别率。
第四章，提出了基于动态工作模板的联机图形属性关系图(ARG)匹配策略，解决了联机图形结构识别中由于存在单笔划的多笔绘制以及笔顺不固定而引起的问题。采用了A~*算法进行元线级别ARG的最优匹配，为此提出了适用于联机图形的代价函数和启发函数。提出了元线级别ARG模板匹配策略，加快了匹配速度。
第五章，提出一种基于Hausdorff距离进行图形符号识别的方法，为以位图形式存放的图形符号的识别和检索提供了一个有效的途径。讨论了Hausdorff距离的性质以及计算方法，指出了在复杂的背景图像中，利用有向Hausdorff距离进行图形符号匹配的适用性，并且提出了一个计算Voronoi曲面的具体算法。提出了在图形符号匹配中，利用事先构造好的多级旋转模型和多级尺度模型的匹配方案。针对由于尺度变换和旋转变换所带来的较大计算开销，本章提出一种基于Hausdorff距离的两阶段识别方法。
第六章，研究了图形符号基元选取的准则，提出了从图形符号到汉语词语的语义映射方法，提出了基于模糊认知图的汉语词语聚类方法，最后提出了利用汉语词语聚类来实现图形符号联想的方法。这种图形符号的语义联想方法可以用来改善信息检索系统的人机交互效率以及用于数据挖掘领域中的信息可视化技术。
The methods of graphics recognition in intelligent human computer interaction are studied thoroughly in this dissertation. Some new pattern recognition techniques in recent years are used in my study, and get improved in my application background. Centered around graphics recognition in intelligent human computer interaction, stroke recognition of online graphics using single classifier, stroke recognition of online graphics using multi classifier combination, structure recognition of online graphics , offline graphics recognition and icon semantics association are discussed respectively in the dissertation.
In chapter 1, the system model of human computer interaction and graphics recognition in human computer interaction is introduced. A review of brief HCI history and the state of the art of HCI are presented. After that, the main achievements of this dissertation are summarized. Finally, the layout of this dissertation is presented.
In chapter 2, Strokes of online graphics are recognized by using three different single classifiers, each of these has separate feature set and different classifying mechanism. These three classifiers are a linear classifier based on fuzzy features, a hierarchy classifier based on features of geometry definitions and a distance classifier based on frequency features of stroke curvature. The performances of the three single classifiers are compared experimentally. The results show that these three stroke classifiers are complementary.
In chapter 3, the method of multi classifier combination based on different pattern features is studied. Based on a theoretical framework, a new scheme of classifier combination is proposed. The performance of this combination scheme is estimated theoretically. This combination scheme is applied to stroke recognition of online graphics. Three single stroke classifiers are combined. Each of these stroke classifiers has separate feature set and different classifying mechanism. The combination scheme is compared with several existing combination schemes experimentally. The results show that this new scheme is better in performance than the others.
In chapter 4, the matching strategy of online graphics attributed relation graphs (ARGs) based on dynamic working matching template is proposed in order to overcome the difficulties caused from stroke order free and strokes which have substrokes. The A* algorithm is used to search the optimism matching of primary stroke ARGs. The cost function and heuristic function adapt to online graphics are proposed. The strategy for primary stroke matching is proposed in order to speed up the matching.
In chapter 5, a method of offline graphics recognition based Hausdorff distance is proposed in order to recognize and retrieve graphics in the form of bitmap. The property and computing method of Hausdorff distance is discussed. The using of directed Hausdorff distance to match graphics in clutter background is verified. An algorithm is used to compute the Voronoi surface. A matching method is proposed, which use multi scale and multi rotation model built up in advance to match the graphics. In order to decrease the computation caused from scaling and rotating, a technique of two stage matching based on Hausdorff distance is proposed.
In chapter 6, the method of icon semantics association is proposed based on Chinese
term clustering. The criterion for choosing primitive of icons is studied. The semantics mapping from icon to Chinese term is proposed. The method of Chinese term clustering based on fuzzy cognitive map (FCM) is proposed too. The method of icon semantics association can be applied to the improving of the information retrieve system based on icon, and can be applied to information visualization technology in data mining.
第一章，介绍了人机交互的系统模型，以及人机交互中的图形识别技术。回顾了人机交互技术的历史与现状。接着介绍了本文的研究成果和本文的章节安排。
第二章，选择了三种不同分类器对联机图形单笔划进行分类。每一种分类器都基于互不相同的特征集合和不同的分类机制，具有较强的独立性。使用的分类器分别为基于模糊特征的线性分类器、使用几何定义特征的分层分类器以及基于曲度频域特征的距离分类器。对三种分类器的性能进行了实验比较，实验结果说明几种分类器具有一定的互补性。
第三章，研究了基于不同模式特征的多分类器联合方法问题。基于一个理论框架，从理论上提出了一种新的分类器联合方法，并对该联合方法进行了性能估计。将这种方法应用于联机几何图形识别问题，联合了三种分类器，每种分类器的机制各不相同，并且都基于不同的模式特征。并且将这种分类器联合方法与几种现有的联合方法进行了实验比较，实验结果说明这种方法具有较高的识别率。
第四章，提出了基于动态工作模板的联机图形属性关系图(ARG)匹配策略，解决了联机图形结构识别中由于存在单笔划的多笔绘制以及笔顺不固定而引起的问题。采用了A~*算法进行元线级别ARG的最优匹配，为此提出了适用于联机图形的代价函数和启发函数。提出了元线级别ARG模板匹配策略，加快了匹配速度。
第五章，提出一种基于Hausdorff距离进行图形符号识别的方法，为以位图形式存放的图形符号的识别和检索提供了一个有效的途径。讨论了Hausdorff距离的性质以及计算方法，指出了在复杂的背景图像中，利用有向Hausdorff距离进行图形符号匹配的适用性，并且提出了一个计算Voronoi曲面的具体算法。提出了在图形符号匹配中，利用事先构造好的多级旋转模型和多级尺度模型的匹配方案。针对由于尺度变换和旋转变换所带来的较大计算开销，本章提出一种基于Hausdorff距离的两阶段识别方法。
第六章，研究了图形符号基元选取的准则，提出了从图形符号到汉语词语的语义映射方法，提出了基于模糊认知图的汉语词语聚类方法，最后提出了利用汉语词语聚类来实现图形符号联想的方法。这种图形符号的语义联想方法可以用来改善信息检索系统的人机交互效率以及用于数据挖掘领域中的信息可视化技术。
The methods of graphics recognition in intelligent human computer interaction are studied thoroughly in this dissertation. Some new pattern recognition techniques in recent years are used in my study, and get improved in my application background. Centered around graphics recognition in intelligent human computer interaction, stroke recognition of online graphics using single classifier, stroke recognition of online graphics using multi classifier combination, structure recognition of online graphics , offline graphics recognition and icon semantics association are discussed respectively in the dissertation.
In chapter 1, the system model of human computer interaction and graphics recognition in human computer interaction is introduced. A review of brief HCI history and the state of the art of HCI are presented. After that, the main achievements of this dissertation are summarized. Finally, the layout of this dissertation is presented.
In chapter 2, Strokes of online graphics are recognized by using three different single classifiers, each of these has separate feature set and different classifying mechanism. These three classifiers are a linear classifier based on fuzzy features, a hierarchy classifier based on features of geometry definitions and a distance classifier based on frequency features of stroke curvature. The performances of the three single classifiers are compared experimentally. The results show that these three stroke classifiers are complementary.
In chapter 3, the method of multi classifier combination based on different pattern features is studied. Based on a theoretical framework, a new scheme of classifier combination is proposed. The performance of this combination scheme is estimated theoretically. This combination scheme is applied to stroke recognition of online graphics. Three single stroke classifiers are combined. Each of these stroke classifiers has separate feature set and different classifying mechanism. The combination scheme is compared with several existing combination schemes experimentally. The results show that this new scheme is better in performance than the others.
In chapter 4, the matching strategy of online graphics attributed relation graphs (ARGs) based on dynamic working matching template is proposed in order to overcome the difficulties caused from stroke order free and strokes which have substrokes. The A* algorithm is used to search the optimism matching of primary stroke ARGs. The cost function and heuristic function adapt to online graphics are proposed. The strategy for primary stroke matching is proposed in order to speed up the matching.
In chapter 5, a method of offline graphics recognition based Hausdorff distance is proposed in order to recognize and retrieve graphics in the form of bitmap. The property and computing method of Hausdorff distance is discussed. The using of directed Hausdorff distance to match graphics in clutter background is verified. An algorithm is used to compute the Voronoi surface. A matching method is proposed, which use multi scale and multi rotation model built up in advance to match the graphics. In order to decrease the computation caused from scaling and rotating, a technique of two stage matching based on Hausdorff distance is proposed.
In chapter 6, the method of icon semantics association is proposed based on Chinese
term clustering. The criterion for choosing primitive of icons is studied. The semantics mapping from icon to Chinese term is proposed. The method of Chinese term clustering based on fuzzy cognitive map (FCM) is proposed too. The method of icon semantics association can be applied to the improving of the information retrieve system based on icon, and can be applied to information visualization technology in data mining.
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