This paper presents an effective hand-crafted texture operator for texture recognition, referred to as oriented star sampling structure based multi-scale ternary pattern (O3S-MTP) which is expected to better represent salient local texture structure. Instead of heuristic code constructions, the proposed O3S-MTP descriptor is defined as a gray-scale invariant texture measure, derived from a general definition of texture in the neighborhood of a 3 × 3 square patch thanks to the flexibility of the graph theory and dominating set. Different from the existing non-oriented local graph structure based texture descriptors, which cannot fully represent the extent of pixel difference, the essence of O3S-MTP operator is to encode the structure of local neighborhood by analyzing the differential excitation and orientation information using new effective oriented star sampling local structures. Extensive experiments on twelve challenging representative widely-used texture datasets show that O3S-MTP can achieve competitive and superior texture classification performance than a large number of recent most promising state-of- the-art texture descriptors. Furthermore, the obtained classification results were statistically validated through the Wilcoxon signed rank test based ranking method.

本文提出了一种用于纹理识别的有效的手工纹理运算符，称为基于定向星采样结构的多尺度三元模式（O3S-MTP），有望更好地表示显着的局部纹理结构。代替启发式代码构造，提议的O3S-MTP描述符被定义为灰度不变纹理度量，得益于图论的灵活性和优势，它是从3×3正方形补丁附近的纹理的一般定义得出的组。与现有的基于非定向局部图结构的纹理描述符不同，后者不能完全表示像素差异的程度，这是O3S-MTP的本质运营商将通过使用新的有效定向星采样局部结构分析差分激励和定向信息来对局部邻域的结构进行编码。在12个具有挑战性且代表性的，广泛使用的纹理数据集上的大量实验表明，与大量最近最有前途的最新纹理描述符相比，O3S-MTP可以实现竞争性和优越的纹理分类性能。此外，通过基于Wilcoxon符号等级检验的排名方法对获得的分类结果进行统计学验证。