The Hellinger-Kakutani metric for pattern recognition

Author

Anh, V.V. ; Tieng, Q. ; Bui, T.D. ; Chen, G.

Author_Institution

Centre in Stat. Sci. & Ind. Math., Queensland Univ. of Technol., Brisbane, Qld., Australia

Volume

2

fYear

1997

fDate

26-29 Oct 1997

Firstpage

430

Abstract

Feature extraction and pattern classification are two key components in a pattern recognition system. In our approach, each image is represented by a 2D Fourier descriptor which is translation-, rotation-, and scale-invariant. We define a new metric, named the Hellinger-Kakutani metric for measuring the distance between two Fourier descriptors for classification. This metric is filtration-invariant, hence can be used on noisy images. The method is applied to a set of 22 Chinese characters, which contains 7 subsets of similar characters. The rate of accurate recognition is then reported

Keywords

Fourier analysis; feature extraction; image classification; image representation; noise; optical character recognition; 2D Fourier descriptor; Chinese characters; Hellinger-Kakutani metric; feature extraction; filtration-invariance; image; noisy images; pattern recognition; representation; rotation-invariance; scale-invariance; translation-invariance; Australia; Euclidean distance; Feature extraction; Fourier transforms; Image databases; Mathematics; Noise robustness; Pattern classification; Pattern recognition; Spatial databases;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1997. Proceedings., International Conference on

Conference_Location

Santa Barbara, CA

Print_ISBN

0-8186-8183-7

Type

conf

DOI

10.1109/ICIP.1997.638800

Filename

638800