Improving the performance of SIFT and CSLBP for image copy detection

Image copy detection is widely used for many applications such as content based image retrieval, image piracy detection, object retrieval, and near duplicate detection. Local features are mostly used to represent the images in database corpus. Initially, keypoints are detected and represented by some distinctive and robust descriptors. The descriptors are computed from the affine local patches around the keypoints. These patches play vital roles for descriptors performance. The main limitation of state-of-the art descriptors include lack of robustness and distinctiveness under severe image distortions, and local patches around keypoints cannot capture distinctive spatial information and structure context. We propose an effective technique for descriptors computation for image copy detection by adding more spatial information from the vicinity of the keypoints. We show experimentally that the performance for retrieving image copies is improved under severe image distortions and attacks. On average, the robustness of SIFT is increased upto 22% and distinctiveness upto 30% for image copy detection task.