Feature extraction is a key step in image processing for pattern recognition and machine learning processes. Its purpose lies in reducing the dimensionality of the input data through the computing of features which accurately describe the original information. In this article, a new feature extraction method based on Discrete Modal Decomposition (DMD) is introduced, to extend the group of space and frequency based features. These new features are called modal features. Initially aiming to decompose a signal into a modal basis built from a vibration mechanics problem, the DMD projection is applied to images in order to extract modal features with two approaches. The first one, called full scale DMD, consists in exploiting directly the decomposition resulting coordinates as features. The second one, called filtering DMD, consists in using the DMD modes as filters to obtain features through a local transformation process. Experiments are performed on image texture classification tasks including several widely used data bases, compared to several classic feature extraction methods. We show that the DMD approach achieves good classification performances, comparable to the state of the art techniques, with a lower extraction time.

特征提取是图像处理中用于模式识别和机器学习过程的关键步骤。其目的在于通过计算可准确描述原始信息的特征来减少输入数据的维数。本文介绍了一种新的基于离散模态分解（DMD）的特征提取方法，以扩展基于空间和频率的特征组。这些新功能称为模态功能。最初旨在将信号分解为基于振动力学问题的模态基础，将DMD投影应用于图像，以便通过两种方法提取模态特征。第一个称为全尺寸DMD，在于直接利用分解所得的坐标作为特征。第二个称为过滤DMD，在于使用DMD模式作为过滤器，以通过局部转换过程获得特征。与几种经典特征提取方法相比，对包括多个广泛使用的数据库的图像纹理分类任务进行了实验。我们表明，DMD方法可实现良好的分类性能，与现有技术水平相当，提取时间更短。