为了提高基于内容图像检索系统的检索速度和准确率,提出一种融合两类线性鉴别分析的方法来提取低维的优化鉴别特征.首先把多类问题转换为多个两类问题,对每个两类问题进行线性鉴别分析,得到鉴别向量；所有的鉴别向量组成鉴别变换矩阵,对图像特征进行投影变换得到鉴别特征；最后用变换后的鉴别特征进行图像检索或分类,得到准确率更高的结果.该方法中鉴别特征空间的维数与类别数相等.与多种特征优化方法进行比较的实验结果表明,采用文中方法可以显著地提高图像检索和图像分类的性能.%In this paper, a method merging 2-class linear discriminant analysis is proposed to capture low-dimensional optimal discriminative features to improve the searching speed and precision of content-base image retrieval systems. First, a multi-class problem is translated to multiple 2-class problems with linear discriminant analysis to estimate a discriminant vector for each. Second, all the discriminant vectors are merged into a discriminant transformation matrix, by which image visual features are transformed into discriminant features. Finally, the discriminant features are employed to gain high precision of image retrieval and classification. The dimensionality of the discriminant features corresponds to the number of classes involved. The experiments, in which our proposed method is compared with various feature optimizing methods, show that the proposed approach improves the performance of image retrieval and classification dramatically.
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