The pattern recognition and computer vision have experienced a prominent progress in feature extraction techniques, judged by the extensive proposed methods in the literature. A big part of these works was devoted to enhance the texture classification performance, regarding the important role of textural analysis in various real-world and challenging applications. Developing discriminant feature extractors requires solid knowledge in machine learning and applied mathematics. However, Local Binary Patterns (LBP) offered much more space to develop enhanced handcrafted descriptors thanks to its simplicity and flexibility. In this paper we introduce a brand new LBP variant referred to as Multi Level Directional Cross Binary Patterns (MLD-CBP). The proposed representation is training-free, low-dimensional, yet discriminative and robust handcrafted operator for texture description. The concept of the proposed MLD-CBP descriptor is based on encoding the most informative directions contained within multi radiuses, which helps in detecting the gray level variations that may occur in different directions. Moreover, the proposed MLD-CBP handcrafted is combined with an automated SVM classifier based on the RBF Kernel, where the $\gamma$ parameter is calculated automatically according to the training images. Conducted experiments on 15 well known and challenging databases of the literature, demonstrate prominent performance and stability compared to the results achieved by 30 recent and most powerful descriptors of the state-of-the-art. This paper provides also a comparative study on the effect of $\gamma$ parameter to show the benefits of automatically tuning this parameter value considering the nature of the database and its size.

模式识别和计算机视觉在特征提取技术上已经取得了显着进步，这在文献中广泛提出的方法中得到了判断。考虑到纹理分析在各种现实世界和具有挑战性的应用中的重要作用，这些作品的很大一部分致力于提高纹理分类性能。开发判别特征提取器需要机器学习和应用数学方面的扎实知识。但是，由于其简单性和灵活性，局部二进制模式（LBP）为开发增强的手工描述符提供了更多空间。在本文中，我们介绍了一种全新的LBP变体，称为多级定向交叉二进制模式（MLD-CBP）。提议的表示形式是无训练的，低维度的，具区分性和鲁棒性的手工操作器，用于纹理描述。提出的MLD-CBP描述符的概念基于对包含在多个半径中的信息最丰富的方向进行编码，这有助于检测可能在不同方向上发生的灰度级变化。此外，将建议的MLD-CBP手工制作与基于RBF内核的自动SVM分类器相结合，其中$\gamma$根据训练图像自动计算参数。在15个众所周知的具有挑战性的文献数据库上进行的实验表明，与30个最新的最强大的描述符相比，该软件具有出色的性能和稳定性。本文还提供了比较研究的效果。$\gamma$ 参数以显示考虑数据库的性质及其大小自动调整此参数值的好处。