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

H. Madokoro, M. Tsukada, K. Sato
2013 Pattern Recognition in Physics  
This paper presents an unsupervised learning-based object category formation and recognition method for mobile robot vision. Our method has the following features: detection of feature points and description of features using a scale-invariant feature transform (SIFT), selection of target feature points using one class support vector machines (OC-SVMs), generation of visual words using self-organizing maps (SOMs), formation of labels using adaptive resonance theory 2 (ART-2), and creation and
more » ... and creation and classification of categories on a category map of counter propagation networks (CPNs) for visualizing spatial relations between categories. Classification results of dynamic images using time-series images obtained using two different-size robots and according to movements respectively demonstrate that our method can visualize spatial relations of categories while maintaining time-series characteristics. Moreover, we emphasize the effectiveness of our method for category formation of appearance changes of objects.
doi:10.5194/prp-1-63-2013 fatcat:hmwzkre3n5bttlcexp2tvkuc7i