Extraction of Shell Texture Feature of Coscinodiscus for Classification Based on Wavelet and PCA

Author

Song, Lina ; Ji, Guangrong ; Chen, Jing

Author_Institution

Dept. of Electron. Eng., Ocean Univ. of China, Qingdao, China

fYear

2009

fDate

25-26 April 2009

Firstpage

282

Lastpage

285

Abstract

Based on wavelet and principal component analysis(PCA), an effective shell texture feature of the coscinodiscus extraction method for classification is proposed in this paper.The feature extraction process involves a normalization of the given image with different sizes followed by shift invariant wavelet transform. The shift invariant feature is computed for subband of wavelet coefficients by PCA. The rate of recognition is calculated in the end. The experiments have proved the method is effective.

Keywords

feature extraction; image texture; pattern classification; principal component analysis; wavelet transforms; PCA; classification; coscinodiscus; principal component analysis; shell texture feature extraction; shift invariant feature; wavelet transform; Artificial intelligence; Electronic mail; Feature extraction; Image analysis; Image converters; Oceans; Principal component analysis; Wavelet analysis; Wavelet coefficients; Wavelet transforms; Image classification; Normalization; Principal component analysis (PCA); Shift invariance; Wavelet transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence, 2009. JCAI '09. International Joint Conference on

Conference_Location

Hainan Island

Print_ISBN

978-0-7695-3615-6

Type

conf

DOI

10.1109/JCAI.2009.75

Filename

5158995