The stationarity of a texture can be considered a fundamental property of images, although the property of stationarity is difficult to define precisely. We propose a stationarity test based on multiscale, locally stationary, 2D wavelets. Three separate experiments were performed to evaluate the capabilities and the limitations of this test. The experiments comprised a chessboard stationarity analysis, two classification tasks, and a psychophysical experiment. The classification tasks were performed on 110 texture images from a texture database. In one subtask, five texture feature vectors were extracted from each image and the classification accuracy of two classical methods compared, whereas in the second subtask, the classification accuracy of several methods was compared to the descriptors defined for each image within the database. In the psychophysical experiment, the correlation between the classification results and observer judgements of texture similarity were determined. It was found that a combination of wavelet shrinkage and rotation-invariant local binary pattern best predicted the observer response. The results show that the proposed stationarity test is able to provide relevant information for texture analysis.

中文翻译：

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纹理图像的视觉和数据平稳性

纹理的平稳性可以被认为是图像的基本属性，尽管平稳性的属性很难精确定义。我们提出了一种基于多尺度、局部平稳、二维小波的平稳性测试。进行了三个独立的实验来评估该测试的能力和局限性。实验包括棋盘平稳性分析、两个分类任务和一个心理物理实验。分类任务是对来自纹理数据库的 110 张纹理图像执行的。在一个子任务中，从每幅图像中提取五个纹理特征向量，并比较两种经典方法的分类精度，而在第二个子任务中，将几种方法的分类精度与数据库中为每幅图像定义的描述符进行比较。在心理物理实验中，确定了分类结果与观察者对纹理相似性的判断之间的相关性。发现小波收缩和旋转不变局部二元模式的组合最能预测观察者响应。结果表明，所提出的平稳性检验能够为纹理分析提供相关信息。