Shape context based image hashing using local feature points

Local feature points have been widely utilized in solving many problems in computer vision, such as robust matching, object detection and classification, due to the fact that they can hold the intrinsic geometric structures of the image content. However, its investigation in the area of image hashing is still limited. In this paper, we propose a novel shape context based image hashing approach using local feature points, by taking advantage of the geometric invariance of local feature points such as SIFT and preserving the intrinsic structure of the image content using shape context. Experimental results clearly show that the proposed hashing is robust to various classic and malicious attacks, due to the virtue of robust salient keypoints detection as well as the shape context feature descriptors. When compared with the current state-of-art block-based image hashing schemes, such as NMF and FJLT hashing, which extract robust features using dimension reduction, experimental results show that the proposed hashing scheme yields better identification performances under geometric attacks such as rotation attacks and brightness changes, and provides comparable performances under classic distortions such as additive noise, blurring and compression.