Adaptive Category Mapping Networks for all-mode topological feature learning used for mobile robot vision

Abstract

This paper presents an adaptive and incremental learning method to visualize series data on a category map. We designate this method as Adaptive Category Mapping Networks (ACMNs). The architecture of ACMNs comprises three modules: a codebook module, a labeling module, and a mapping module. The codebook module converts input features into codebooks as low-dimensional vectors using Self-Organizing Maps (SOMs). The labeling module creates labels as a candidate of categories based on the incremental learning of Adaptive Resonance Theory (ART). The mapping module visualizes spatial relations among categories on a category map using Counter Propagation Networks (CPNs). ACMNs actualize supervised, semi-supervised, and unsupervised learning as all-mode learning to switch network structures including connections. The experimentally obtained results obtained using two open datasets reveal that the recognition accuracy of our method is superior to that of the former method. Moreover, we address applications of the visualizing function using category maps.