Learning Membership Functions in a Function-Based Object Recognition System
Learning Membership Functions in a Function-Based Object Recognition System
复制标题
学习基于函数的对象识别系统中的隶属函数
DOI：
10.1613/jair.236
复制
发表时间：
1995
期刊：
影响因子：
--
通讯作者：
L. Stark
中科院分区：
文献类型：
--
作者：
K. Woods;D. Cook;L. Hall;K. Bowyer;L. Stark
基于功能的识别系统通过推理对象对预期功能的支持程度来识别类别级别的对象。这样的系统自然而然地将“善的衡量”或“成员价值”与公认的对象联系在一起。这种良好性的测量是将个体测量或隶属度值结合在一起的结果，这些测量或隶属度值可能来自对对象形状的不同属性的许多原始评估。当评估对象的特定物理属性的基元时，使用隶属度函数来计算隶属度值。在以前版本的识别系统中，每个基本评估的隶属函数都是由系统设计者手工创建的。在这篇文章中，我们为粗略系统提供了一个学习组件，称为Omlet，它自动学习隶属函数，给出一组用它们所需的类别度量标记的示例对象。该学习算法一般适用于通过与或树结构将低级别隶属度值组合以给出最终的总体隶属度值的任何问题。
Functionality-based recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function. Such systems naturally associate a "measure of goodness" or "membership value" with a recognized object. This measure of goodness is the result of combining individual measures, or membership values, from potentially many primitive evaluations of diffierent properties of the object's shape. A membership function is used to compute the membership value when evaluating a primitive of a particular physical property of an object. In previous versions of a recognition system known as GRUFF, the membership function for each of the primitive evaluations was hand-crafted by the system designer. In this paper, we provide a learning component for the GRUFF system, called OMLET, that automatically learns membership functions given a set of example objects labeled with their desired category measure. The learning algorithm is generally applicable to any problem in which low-level membership values are combined through an and-or tree structure to give a final overall membership value.