Secure SIFT-based sparse representation for image copy detection and recognition

In this paper, we formulate the problems of image copy detection and image recognition in terms of sparse representation. To achieve robustness, security, and efficient storage of image features, we propose to extract compact local feature descriptors via constructing the basis of the SIFT-based feature vectors extracted from the secure SIFT domain of an image. Image copy detection can be efficiently accomplished based on the sparse representations and reconstruction errors of the features extracted from an image possibly manipulated by signal processing or geometric attacks. For image recognition, we show that the features of a query image can be represented as sparse linear combinations of the features extracted from the training images belonging to the same cluster. Hence, image recognition can also be cast as a sparse representation problem. Then, we formulate our sparse representation problem as an l1-minimization problem. Promising results regarding image copy detection and recognition have been verified, respectively, through the simulations conducted on several content-preserving attacks defined in the Stirmark benchmark and Caltech-101 dataset.