A Rotation, Translation, and Scale-Invariant Approach to Content-Based Image Retrieval

We describe a method for computing an image signature, suitable for content-based retrieval from image databases. The signature is extracted from the Fourier power spectrum by performing a mapping from cartesian to logarithmic-polar coordinates, projecting this mapping onto two 1D signature vectors, and computing their power spectra coefficients. Similar to wavelet-based approaches, this representation isholisticand, thus, provides a compact description of all image aspects, including shape, texture, and color. Furthermore, it has the advantage of being invariant to 2D rigid transformations, such as any combination of rotation, scaling, and translation. Experiments have been conducted on a database of 2082 images extracted from various news video clips. Results confirm invariance to 2D rigid transformations, as well as high resilience to more general affine and projective transformations. Moreover, the signature appears to capture perceptually relevant image features, in that it allows successful database querying using example images which have been subject to arbitrary camera and subject motion.