DCT-based adaptive metric learning model using asymptotic local information measure

Takami Satonaka, Takaaki Baba, Takayuki Chikamura, Tatsuo Otsuki, Teresa H. Meng

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

We present an adaptive metric learning vector quantization procedure based on the discrete-cosine transform (DCT) for accurate face recognition used in multimedia application. Since the set of learning samples may be small, we employ a mixture model of prior distributions. The model selection method, which minimizes the cross entropy between the real distribution and the modeled one, is presented to optimize the mixture number and local metric parameters. The structural risk minimization is used to facilitate an asymptotic approximation of the cross entropy for models of fixed complexity. We also provide a formula to estimate the model complexity derived from the minimum description length criterion. The structural risk minimization method proposed achieves an recognition error rate of 2.29% using the ORL database, which is better than previously reported numbers using the Karhunen-Loeve transform convolution network, the hidden Marcov model and the eigenface model.

本文言語English
ホスト出版物のタイトルNeural Networks for Signal Processing - Proceedings of the IEEE Workshop
Place of PublicationPiscataway, NJ, United States
出版社IEEE
ページ521-530
ページ数10
出版ステータスPublished - 1997
外部発表はい
イベントProceedings of the 1997 7th IEEE Workshop on Neural Networks for Signal Processing, NNSP'97 - Amelia Island, FL, USA
継続期間: 1997 9 241997 9 26

Other

OtherProceedings of the 1997 7th IEEE Workshop on Neural Networks for Signal Processing, NNSP'97
CityAmelia Island, FL, USA
Period97/9/2497/9/26

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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