Two-stage computational cost reduction algorithm based on mahalanobis distance approximations

Fang Sun, Shin'ichiro Omachi, Nei Kato, Hirotomo Aso, Shun'ichi Kono, Tasuku Takagi

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

For many pattern recognition methods, high recognition accuracy is obtained at very high expense of computational cost. In this paper, a new algorithm that reduces the computational cost for calculating discriminant function is proposed. This algorithm consists of two stages which are feature vector division and dimensional reduction. The processing of feature division is based on characteristic of covariance matrix. The dimensional reduction in the second stage is done by an approximation of the Mahalanobis distance. Compared with the well-known dimensional reduction method of K-L expansion, experimental results show the proposed algorithm not only reduces the computational cost but also improves the recognition accuracy.

Original languageEnglish
Pages (from-to)696-699
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Volume15
Issue number2
Publication statusPublished - 2000

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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