Object Topological Character Acquisition by Inductive Learning
Wei Hui Liping Yu Yiran Wei
Understanding the shape and structure of objects is undoubtedly extremely
important for object recognition, but the most common pattern recognition
method currently used is machine learning, which often requires a large number
of training data. The problem is that this kind of object-oriented learning
lacks a priori knowledge. The amount of training data and the complexity of
computations are very large, and it is hard to extract explicit knowledge after
learning. This is typically called "knowing how without knowing why". We
adopted a method of inductive learning, hoping to derive conceptual knowledge
of the shape of an object and its formal representation based on a small number
of positive examples. It is clear that implementing object recognition is not
based on simple physical features such as colors, edges, textures, etc., but on
their common geometry, such as topologies, which are stable, persistent, and
essential to recognition. In this paper, a formal representation of topological
structure based on object's skeleton (RTS) was proposed and the induction
process of "seeking common ground" is realized. This research helps promote the
method of object recognition from empiricism to rationalism.