Fast feature extraction approach for multi-dimension feature space problems

Alaa Sagheer, Naoyuki Tsuruta, Rin Ichiro Taniguchi, Daisaku Arita, Sakashi Maeda

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

3 被引用数 (Scopus)

抄録

Recently, we proposed a fast feature extraction approach denoted FSOM utilizes Self Organizing Map (SOM). FSOM [1] overcomes the slowness of traditional SOM search algorithm. We investigated the superiority of the new approach using two lip reading data sets which require a limited feature space as the experiments in [1] showed. In this paper, we continue FSOM investigation but using an RGB face recognition database across different poses and different lighting conditions. We believe that such data sets require multi-dimensional feature space to extract the information included in the original data in an effective way especially if you have a big number of classes. Again, we show here how is FSOM reduces the feature extraction time of traditional SOM drastically while preserving same SOM's qualities.

本文言語英語
ホスト出版物のタイトルProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
ページ417-420
ページ数4
DOI
出版ステータス出版済み - 2006
イベント18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, 中国
継続期間: 8 20 20068 24 2006

出版物シリーズ

名前Proceedings - International Conference on Pattern Recognition
3
ISSN(印刷版)1051-4651

その他

その他18th International Conference on Pattern Recognition, ICPR 2006
国/地域中国
CityHong Kong
Period8/20/068/24/06

All Science Journal Classification (ASJC) codes

  • コンピュータ ビジョンおよびパターン認識

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