Title :
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
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;
Conference_Titel :
Artificial Intelligence, 2009. JCAI '09. International Joint Conference on
Conference_Location :
Hainan Island
Print_ISBN :
978-0-7695-3615-6
DOI :
10.1109/JCAI.2009.75
