Object Topological Character Acquisition by Inductive Learning
This work addresses the issue of data inefficiency and lack of interpretability in object recognition for computer vision applications, representing an incremental step towards rationalist methods.
The paper tackles the problem of object recognition requiring large training data and lacking explicit knowledge by proposing an inductive learning method that derives conceptual shape knowledge from few positive examples, achieving a formal representation of topological structure based on object skeletons.
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.