Huh Seungil, Lee Donghun
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 USA.
IEEE Trans Neural Netw. 2010 Dec;21(12):1990-6. doi: 10.1109/TNN.2010.2090047. Epub 2010 Nov 11.
We propose signature linear discriminant analysis (signature-LDA) as an extension of LDA that can be applied to signatures, which are known to be more informative representations of local image features than vector representations, such as visual word histograms. Based on earth mover's distances between signatures, signature-LDA does not require vectorization of local image features in contrast to LDA, which is one of the main limitations of classical LDA. Therefore, signature-LDA minimizes the loss of intrinsic information of local image features while selecting more discriminating features using label information. Empirical evidence on texture databases shows that signature-LDA improves upon state-of-the-art approaches for texture image classification and outperforms other feature selection methods for local image features.
我们提出了签名线性判别分析（signature-LDA），作为线性判别分析（LDA）的一种扩展，它可应用于签名。众所周知，签名是局部图像特征比向量表示（如图视单词直方图）更具信息性的表示形式。基于签名之间的推土机距离，与经典LDA的主要局限之一（即需要对局部图像特征进行矢量化）不同，signature-LDA不需要对局部图像特征进行矢量化。因此，signature-LDA在利用标签信息选择更具区分性特征的同时，将局部图像特征固有信息的损失降至最低。纹理数据库的经验证据表明，signature-LDA改进了纹理图像分类的现有方法，并且优于其他用于局部图像特征的特征选择方法。
