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

Li-Na Song, Guangrong Ji, Jing Chen
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引用次数: 1

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.
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基于小波和主成分分析的尾盘壳纹理特征提取及其分类
基于小波变换和主成分分析(PCA),提出了一种有效提取尾盘壳纹理特征的分类方法。特征提取过程包括对给定的不同大小的图像进行归一化,然后进行平移不变小波变换。利用主成分分析法计算了小波系数子带的平移不变特征。最后计算了图像的识别率。实验证明了该方法的有效性。
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