Besides a high distinctiveness, robustness (or invariance) to image degradations is very desirable for texture feature extraction methods in real-world applications. In this paper, focus is on making arbitrary texture descriptors invariant to blur which is often prevalent in real image data. From previous work, we know that most state-of-the-art texture feature extraction methods are unable to cope even with minor blur degradations if the classifier’s training stage is based on idealistic data. However, if the training set suffers similarly from the degradations, the obtained accuracies are significantly higher. Exploiting that knowledge, in this approach the level of blur of each image is increased to a certain threshold, based on the estimation of a blur measure. Experiments with synthetically degraded data show that the method is able to generate a high degree of blur invariance without loosing too much distinctiveness. Finally, we show that our method is not limited to ideal Gaussian blur.

中文翻译：

---

使纹理描述符不变以模糊。

除了具有很高的区别性之外，对于现实应用中的纹理特征提取方法，非常需要图像退化的鲁棒性（或不变性）。在本文中，重点是使任意纹理描述符对模糊不变，这在真实图像数据中通常很普遍。从以前的工作中，我们知道，如果分类器的训练阶段是基于理想数据，则大多数最新的纹理特征提取方法都无法应对，即使有轻微的模糊降级。但是，如果训练集遭受类似的降级，则获得的准确度会更高。利用该知识，在这种方法中，基于对模糊量度的估计，每个图像的模糊度会增加到某个阈值。对合成退化数据进行的实验表明，该方法能够产生高度的模糊不变性，而不会失去太多的独特性。最后，我们证明了我们的方法不仅限于理想的高斯模糊。