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Abstract: This paper describes a visual shape descriptor based on the sectors and shape context of contour lines to represent the image local features used for image matching. The proposed descriptor consists of two-component feature vectors. First, the local region is separated into sectors and their gradient magnitude and orientation values are extracted; a feature vector is then constructed from these values. Second, local shape features are obtained using the shape context of contour lines. Another feature vector is then constructed from these contour lines. The proposed approach calculates the local shape feature without needing to consider the edges. This can overcome the difficulty associated with textured images and images with ill-defined edges. The combination of two-component feature vectors makes the proposed descriptor more robust to image scale changes, illumination variations and noise. The proposed visual shape descriptor outperformed other descriptors in terms of the matching accuracy: 14.525% better than SIFT, 21% better than PCA-SIFT, 11.86% better than GLOH, and 25.66% better than the shape context. [Copyright &y& Elsevier]