The University of British Columbia

The ability to retrieve images from databases is of great importance for a wide range of applications. We present a new method for image identification and retrieval that enables the recovery of original images even after size-conserving rotation or flipping operations. Our method uses the correlation of wavelet packets coefficients to create image signatures. It first computes a basic signature for the original image by summing the correlation values along all frequency bands. Possible image rotation/flipping cases are then used to generate additional short signatures that are added to the basis signature, to identify geometric transformations. Simulation results show that our method has image retrieval rates between 88.6% and 91.7%, and geometric transformation recognition of 99.57%.