Selection of SIFT feature points for scene description in robot vision
Yuy, Masahiro Tsukada, Hirokazu Madokoro, Kazuhito Sato · 2010
This paper presents an unsupervised learning-based method for selection of feature points and object category classification to apply to a vision-based mobile robot. Our method has the following four capabilities. First, our method can localize target feature points using One Class-Support Vector Machines (OC-SVMs) without previous setting of boundary information. Second, our method can generate labels as a candidate of categories for input images while maintaining stability and plasticity together. Third, automatic labeling of category maps can be realized using labels created using Adaptive Resonance Theory-2 (ART-2) as teaching signals for Counter Propagation Networks (CPNs). Fourth, our method can present the diversity of appearance changes for visualizing spatial relations of each category on a two-dimensional map of CPNs. Through category classification experiments, we evaluate our method using the Caltech-256 object category dataset and time-series images taken by a camera on a mobile robot.