Unsupervised and adaptive category classification for a vision-based mobile robot

Abstract

This paper presents an unsupervised category classification method for time-series images that combines incremental learning of Adaptive Resonance Theory-2 (ART-2) and self-mapping characteristic of Counter Propagation Networks (CPNs). Our method comprises the following procedures: 1) generating visual words using Self-Organizing Maps (SOM) from 128-dimensional descriptors in each feature point of a Scale-Invariant Feature Transform (SIFT), 2) forming labels using unsupervised learning of ART-2, and 3) creating and classifying categories on a category map of CPNs for visualizing spatial relations between categories. We use a vision system on a mobile robot for taking time-series images. Experimental results show that our method can classify objects into categories according to their change of appearance during the movement of a robot.