Contour and skeleton are two main stream representations for shape recognition in the literature. It has been shown that such two representations convey complementary information, however combining them in a nature way is nontrivial, as they are generally abstracted by different structures (closed string
graph), respectively. This paper aims at addressing the shape recognition problem by combining contour and skeleton into a mid-level of shape representation. To form a mid-level representation for shape contours, a recent work named
ragments (BCF) is adopted; While for skeleton, a new mid-level representation named
aths (BSP) is proposed, which is formed by pooling the skeleton codes by encoding the skeleton paths connecting pairs of end points in the skeleton. Finally, a compact shape feature vector is formed by concatenating BCF with BSP and fed into a linear SVM classifier to recognize the shape. Although such a concatenation is simple, the SVM classifier can automatically learn the weights of contour and skeleton features to offer discriminative power. The encouraging experimental results demonstrate that the proposed new shape representation is effective for shape classification and achieves the state-of-the-art performances on several standard shape benchmarks.