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

Abstract

Ahstract-This paper presents an unsupervised category classification method for time-series images that combines incre mental 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 (SIF T), 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.

Unsupervised and self-mapping category formation and semantic object recognition for mobile robot vision used in an actual environment

“…Furthermore, the combination of ART-2 and CPNs enables unsupervised category formation that labels a large quantity of images in each category automatically. Table 4 shows parameters of OC-SVMs, ART-2, and CPNs based on our former study (Tsukada et al, 2010(Tsukada et al, , 2011Madokoro et al, 2011b). Herein, we compared our method (Tsukada et al, 2010) with the method proposed by Chen et al (2009) using the Caltech-256 object category dataset (Griffin et al, 2007).…”

“…Table 4 shows parameters of OC-SVMs, ART-2, and CPNs based on our former study (Tsukada et al, 2010(Tsukada et al, , 2011Madokoro et al, 2011b). Herein, we compared our method (Tsukada et al, 2010) with the method proposed by Chen et al (2009) using the Caltech-256 object category dataset (Griffin et al, 2007). We obtained a result that the performance of our method was superior to their method, although the target dataset was aimed at generic object recognition.…”