This letter presents a noise-robust descriptor by exploring a set of local contrast patterns (LCPs) via global measures for texture classification. To handle image noise, the directed and undirected difference masks are designed to calculate three types of local intensity contrasts: directed, undirected, and maximum difference responses. To describe pixel-wise features, these responses are separately quantized and encoded into specific patterns based on different global measures. These resulting patterns (i.e., LCPs) are jointly encoded to form our final texture representation. Experiments are conducted on the well-known Outex and CUReT databases in the presence of high levels of noise. Compared to many state-of-the-art methods, the proposed descriptor achieves superior texture classification performance while enjoying a compact feature representation.