【摘 要】
：
文章提出了一种基于非负矩阵分解（Nonnegative Matrix Factorization.NMF）和多特征融合的图像检索模型。通过提取图像的颜色和纹理特征,进行NMF分解,得到NMF的基矩阵和样本的系
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文章提出了一种基于非负矩阵分解（Nonnegative Matrix Factorization.NMF）和多特征融合的图像检索模型。通过提取图像的颜色和纹理特征,进行NMF分解,得到NMF的基矩阵和样本的系数矩阵。利用二维主成分分析（2DPCA）的思想对系数矩阵降维,然后通过特征加权的方法比较检索结果。文章使用500幅人物图像组成的图像库进行试验,该方法利用了图像的多个特征和2DPCA思想,使得文章中的方法提高了检索的查准率,而且检索速度优于非负矩阵分解和二维主成分分析。
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