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Regular Research Article 24 Jul 2013
2Meiji Co., Ltd., Tokyo, Japan
Received: 27 May 2013 – Revised: 05 Jul 2013 – Accepted: 08 Jul 2013 – Published: 24 Jul 2013 Abstract. 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 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.
Citation: Madokoro, H., Tsukada, M., and Sato, K.: Unsupervised and self-mapping category formation and semantic object recognition for mobile robot vision used in an actual environment, Pattern Recogn. Phys., 1, 63-74, doi:10.5194/prp-1-63-2013, 2013.
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