基于IPC号的热门共性技术机遇的挖掘方法

为挖掘同时具有热门特征和共性特征的行业技术机遇,设计了一种基于国际专利分类号(IPC号)词频和共现情况的行业热门共性技术机遇挖掘方法。通过对检索下载的行业IPC号进行清洗和格式化处理,抽取不同时间段的IPC号,采用频次分析以及一元线性回归方程计算技术热门程度,分析IPC号共现情况,结合度中心性计算技术的共性程度;利用四象限图展示不同对象的隶属区间,并找出同时具有热门特性和共性特征的技术分类号,以此作为行业技术机遇。工业清洗行业技术机遇的挖掘实例表明,该方法是有效和可行的。     

Silhouettes Based Human Action Recognition in Video via Procrustes Analysis and Fisher Vector Coding

This paper proposes a framework for human action recognition based on procrustes analysis and Fisher vector coding(FVC). Firstly, we applied a pose feature extracted from silhouette image by employing Procrustes analysis and local preserving projection(LPP). Secondly, the extracted feature can preserve the discriminative shape information and local manifold structure of human pose and is invariant to translation, rotation and scaling. Finally, after the pose feature was extracted, a recognition framework based on FVC and multi-class supporting vector machine was employed to classify the human action. Experimental results on benchmarks demonstrate the effectiveness of the proposed method.