Three Precise Spatial Signatures for Image Copy Recall
Saif alZahir, Hassan Bayaa · 2018
The literature is scarce on content-based copy detection and recall (CBCD). Methods on this categories attempt to exploit one or more feature(s) of images such as shape, color, or texture to recall a query image. The performance of these methods is satisfactory but not perfect. In this paper, we present three fast image copy recall algorithms from a database with perfect results. These algorithms are specifically developed to overcome the problems of copies of same image with different amounts of illumination intensities; similar images with trifling difference(s); and a limited image flipping cases in databases. The algorithms are based on spatial signatures that uniquely represent the images in the database as well as the query image to be recalled. We tested our algorithms on a set of 23, 443 images from LIVE, PIE databases, ORL Database of Faces (the AT&T laboratories Cambridge Database), Caltech-UCSD Birds database and our miscellany of images. Simulations results show that in each case, the query image was recalled with perfect accuracy. Finally, as our results show 100% accuracy, it was unnecessary to compare our results with previously published methods.