Learning Membership Functions in a Function-Based Object Recognition System
<|reference_start|>Learning Membership Functions in a Function-Based Object Recognition System: 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 different 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.<|reference_end|>
@article{woods1995learning,
    title={Learning Membership Functions in a Function-Based Object Recognition
  System},
    author={K. Woods, D. Cook, L. Hall, K. Bowyer, L. Stark},
    journal={Journal of Artificial Intelligence Research, Vol 3, (1995),
  187-222},
    year={1995},
    archivePrefix={arXiv},
    eprint={cs/9510103},
    primaryClass={cs.AI}
}