一种基于部件功用性语义组合的家庭日常工具分类方法
发布时间：2018-12-29 16:15
【摘要】：为满足人机共融环境下机器智能对工具功用性认知的需要,模拟人类自底向上的认知方式,设计了一种基于部件功用性语义组合的聚类方法,来对家庭日常工具进行表示与建模.首先,设计了工具功用性部件边缘表示方法并基于结构随机森林加以建模.然后基于功用性部件组合思想,设计了高层语义空间上联合各部件显著度的工具整体表示方法并采用聚类方式构建工具功用性字典.在线检测阶段,联合测试样本各功用性部件的显著度,利用其与工具功用性字典的距离残差对工具进行分类判别.在实验中,将7种功用性部件组合聚类形成5类工具,当各类工具选取不同核值时,分类精度可达90%以上,即使各类工具的核值固定为3,分类精度也在85%以上.实验结果表明,相较于传统的特征表示方式,功用性语义的加入使机器人深化了对工具功能的认知,基于功用性部件组合的字典表示使得家庭常见工具的分类精度和效率明显提升,且实现了工具间功能相似性测算和最优替代工具查找.
[Abstract]:In order to meet the needs of machine intelligence for tool functional cognition in human-computer inclusive environment, a clustering method based on component functional semantic combination was designed to simulate human bottom-up cognition. To represent and model everyday family tools. Firstly, the edge representation method of utility components is designed and modeled based on structured random forest. Then, based on the idea of functional component combination, a tool overall representation method combining the saliency of each component in high-level semantic space is designed, and a tool functional dictionary is constructed by clustering method. In the online detection stage, the salience of each functional part of the sample is jointly tested, and the distance residuals between the sample and the tool function dictionary are used to classify the tool. In the experiment, the 7 kinds of functional components are grouped into five kinds of tools. When different kernel values are selected, the classification accuracy can reach more than 90%. Even if the kernel value of all kinds of tools is fixed at 3, the classification accuracy is more than 85%. The experimental results show that, compared with the traditional feature representation, the addition of functional semantics makes the robot deepen its understanding of the tool function. The dictionary representation based on functional component combination improves the classification accuracy and efficiency of common family tools, and realizes the function similarity measurement and optimal alternative tool search among tools.
【作者单位】： 燕山大学信息科学与工程学院;中国科学院自动化研究所复杂系统管理与控制国家重点实验室;河北省计算机虚拟技术与系统集成重点实验室;
【基金】：国家自然科学基金(61305113) 河北省自然科学基金(F2016203358)
【分类号】：TP242
，
本文编号：2395050
[Abstract]:In order to meet the needs of machine intelligence for tool functional cognition in human-computer inclusive environment, a clustering method based on component functional semantic combination was designed to simulate human bottom-up cognition. To represent and model everyday family tools. Firstly, the edge representation method of utility components is designed and modeled based on structured random forest. Then, based on the idea of functional component combination, a tool overall representation method combining the saliency of each component in high-level semantic space is designed, and a tool functional dictionary is constructed by clustering method. In the online detection stage, the salience of each functional part of the sample is jointly tested, and the distance residuals between the sample and the tool function dictionary are used to classify the tool. In the experiment, the 7 kinds of functional components are grouped into five kinds of tools. When different kernel values are selected, the classification accuracy can reach more than 90%. Even if the kernel value of all kinds of tools is fixed at 3, the classification accuracy is more than 85%. The experimental results show that, compared with the traditional feature representation, the addition of functional semantics makes the robot deepen its understanding of the tool function. The dictionary representation based on functional component combination improves the classification accuracy and efficiency of common family tools, and realizes the function similarity measurement and optimal alternative tool search among tools.
【作者单位】： 燕山大学信息科学与工程学院;中国科学院自动化研究所复杂系统管理与控制国家重点实验室;河北省计算机虚拟技术与系统集成重点实验室;
【基金】：国家自然科学基金(61305113) 河北省自然科学基金(F2016203358)
【分类号】：TP242
，
本文编号：2395050
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