This paper proposes a novel multi-scale descriptor for shape recognition. The contour of shape is represented by a sequence of sample points with uniform spacing. Straight lines connected between two moving contour points are used to cut the shape. The lengths of the contour segments between the two sampled contour points determine the levels of scales. Then the geometric features of the cut contour and the interior texture features around the straight lines are extracted at each scale. This method not only has the powerful discriminability to describe a shape from coarse to fine, but also is invariant to scale, rotation, translation and mirror transformations. Experiments conducted on five image datasets (COIL-20, Flavia, Swedish, Leaf100 and ETH-80) demonstrate that the proposed method significantly outperforms the state-of-the-art methods.