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

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@InProceedings{Tsukada:2010:ijcnn,

• 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

Citations