An invariant feature extraction method based on smoothed local binary pattern (SLBP) is proposed for strip steel surface defect images. SLBP proposed in this paper is a developed version of local binary pattern (LBP). It is determined by the sign of the difference between weighted grays in local neighborhood. SLBP has the ability of noise smoothing. In this paper, invariant features are obtained by concentric discrete square sampling template (CDSST). Firstly, defect images are resampled on CDSST by the way of coordinate mapping. Then, invariant features in scale, rotation, illumination and translation are extracted by combining two types of SLBP images and gray-level co-occurrence matrix. Experimental results show that this novel feature extraction method not only can extract features with scale, rotation, illumination and translation invariance, but also can effectively suppress noise and maintain high classification accuracy.