Selecting episode representation frame is one of the important processes in video semantic analysis and content-based video retrieval.
情节代表帧选取方法是视频语义分析和基于内容的视频检索的很重要的方法。
In this paper, we realize the representation, reasoning and implementation of knowledge based discrete event simulation models by means of object oriented frame language.
利用面向对象的框架语言实现了知识基离散仿真模型的表达、推理和实施。
The representation of client requirement knowledge based on frame - rule and its inference process are presented, the product knowledge index and retrieve are analyzed.
提出了客户需求知识的框架-规则表示方法及推理过程，对产品知识索引与检索方式进行分析。
An algorithm for selecting episode representation frames by using an approach of key frame extraction based on multiple characters and C-Mean fuzzy clustering is detailed in the paper.
该文在子镜头的关键帧提取方法基础上，利用模糊c -均值聚类算法，实现了一种基于子镜头聚类的情节代表帧选取方法。
Firstly, the paper analyzes theoretically the case-based reasoning, then studies the characters of taking-apart craft and presents the frame representation of locomotive taking-apart craft.
首先对基于事例推理进行了理论分析，研究了解体工艺的特征，提出了内燃机车解体工艺知识的事例框架表示法。
Based on the model management and model-built dynamic rules, this paper presents the knowledge-based model frame representation method, and the application example is given.
根据模型管理和模型建立动态性原则的要求，提出了模型知识化的框架表示法，并给出了应用实例。
We present a framework of model management systems based on knowledge and analyse the necessity and adaptability of the frame knowledge representation for model management systems.
提出了一种基于知识的模型管理系统实现框架，分析了模型管理系统的知识方法引入的必要性和框架知识表示结构对于模型知识表示的适应性。
We present a framework of model management systems based on knowledge and analyse the necessity and adaptability of the frame knowledge representation for model management systems.
提出了一种基于知识的模型管理系统实现框架，分析了模型管理系统的知识方法引入的必要性和框架知识表示结构对于模型知识表示的适应性。
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