Presentation 2010-01-18
Shape group representation using higher-rank SOM with affine-invariant distance measure
Sho YAKUSHIJI, Takashi OHKUBO, Tetsuo FURUKAWA,
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Abstract(in English) This study aims to develop an observation-invariant classification algorithm of multi-class models. The task of the architecture is to classify a set of datasets, each of which is transformed differently when it is observed. Therefore it is required to remove observation-dependent component from the datasets. The higher-rank of SOM (SOM^n or 'SOM of SOMs') was employed for multi-datasets classification task, and an observation-invariant distance measured was introduced into the SOM^n algorithm. The algorithm was applied to a classification task of a set of shapes, which are transformed by affine deformation.
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Keyword(in English) self-organizing maps / distance measure / shape representaion / shape classification
Paper # NC2009-74
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Committee NC
Conference Date 2010/1/11(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Shape group representation using higher-rank SOM with affine-invariant distance measure
Sub Title (in English)
Keyword(1) self-organizing maps
Keyword(2) distance measure
Keyword(3) shape representaion
Keyword(4) shape classification
1st Author's Name Sho YAKUSHIJI
1st Author's Affiliation Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology()
2nd Author's Name Takashi OHKUBO
2nd Author's Affiliation Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology
3rd Author's Name Tetsuo FURUKAWA
3rd Author's Affiliation Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology
Date 2010-01-18
Paper # NC2009-74
Volume (vol) vol.109
Number (no) 363
Page pp.pp.-
#Pages 6
Date of Issue