As the most important step in shape-based image retrieval, the description of image contour should reflect the information of global shape and key points, and be robust to random noise. This paper proposes a new image retrieval method based on contour reconstruction and feature point chord length. First, the contour of the shape is extracted, and in order to reduce the distortion caused by random noise, the contour is reconstructed by analyzing the energy retention rate. Then, base on the new defined supportive region, the feature intensity is calculated at each point of the contour to extract the valid feature points. After that, the contour feature function is structured by using the chord length between contour points and corresponding feature points. Finally, the shape descriptors are processed to meet the invariance property. A significant amount of experiments show that, in both normal and noisy sample sets, the proposed method demonstrates better performance compared with other seven techniques.