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

Created by W.Langdon from gp-bibliography.bib Revision:1.4394

  author =       "Masahiro Tsukada and Hirokazu Madokoro and 
                 Kazuhito Sato",
  title =        "Unsupervised and adaptive category classification for
                 a vision-based mobile robot",
  booktitle =    "International Joint Conference on Neural Networks
                 (IJCNN 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-6917-8",
  abstract =     "This paper presents an unsupervised category
                 classification method for time-series images that
                 combines incremental 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-Organising Maps (SOM) from
                 128-dimensional descriptors in each feature point of a
                 Scale-Invariant Feature Transform (SIFT), 2) forming
                 labels using unsupervised learning of ART-2, and 3)
                 creating and classifying categories on a category map
                 of CPNs for visualising 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.",
  DOI =          "doi:10.1109/IJCNN.2010.5596323",
  notes =        "WCCI 2010. Also known as \cite{5596323}",

Genetic Programming entries for Masahiro Tsukada Hirokazu Madokoro Kazuhito Sato