Department of Computer Science, Prince Sultan University, Riyadh, Saudi Arabia.
IEEE Trans Image Process. 2011 Nov;20(11):3270-9. doi: 10.1109/TIP.2011.2143422. Epub 2011 Apr 19.
This paper proposes a new approach to extract global image features for the purpose of texture classification. The proposed texture features are obtained by generating an estimated global map representing the measured intensity similarity between any given image pixel and its surrounding neighbors within a certain window. The intensity similarity map is an average representation of the texture-image dominant neighborhood similarity. The estimated dominant neighborhood similarity is robust to noise and referred to as image dominant neighborhood structure. The global rotation-invariant features are then extracted from the generated image dominant neighborhood structure. Features obtained from the local binary patterns (LBPs) are then extracted in order to supply additional local texture features to the generated features from the dominant neighborhood structure. Both features complement each other. The experimental results on representative texture databases show that the proposed method is robust to noise and can achieve significant improvement in terms of the obtained classification accuracy in comparison to the LBP method. In addition, the method classification accuracy is comparable to the two recent LBP extensions: dominant LBP and completed LBP.
本文提出了一种新的方法来提取全局图像特征，用于纹理分类。所提出的纹理特征是通过生成一个估计的全局图来获得的，该全局图表示在给定窗口内任意给定图像像素与其周围邻居之间的测量强度相似性。强度相似性图是纹理-图像主导邻域相似性的平均表示。估计的主导邻域相似性对噪声具有鲁棒性，被称为图像主导邻域结构。然后从生成的图像主导邻域结构中提取全局旋转不变特征。然后提取局部二值模式 (LBP) 得到的特征，以为主导邻域结构生成的特征提供额外的局部纹理特征。这两种特征相辅相成。在具有代表性的纹理数据库上的实验结果表明，与 LBP 方法相比，该方法对噪声具有鲁棒性，可以显著提高获得的分类准确性。此外，该方法的分类准确性可与两种最近的 LBP 扩展方法相媲美：主导 LBP 和完成 LBP。
