Invariant content-based image retrieval by wavelet energy signatures

An effective rotation and scale invariant log-polar wavelet texture feature for image retrieval was proposed. The feature extraction process involves a log-polar transform followed by an adaptive row shift invariant wavelet packet transform. The log-polar transform converts a given image into a rotation and scale invariant but row-shifted image, which is then passed to the adaptive row shift invariant wavelet packet transform to generate adaptively some subbands of rotation and scale invariant wavelet coefficients with respect to an information cost function. An energy signature is computed for each subband of these wavelet coefficients. In order to reduce feature dimensionality, only the most dominant log-polar wavelet energy signatures are selected as feature vector for image retrieval. The whole feature extraction process is quite efficient and involves only O(n·log n) complexity. Experimental results show that this rotation and scale invariant texture feature is effective and outperforms the traditional wavelet packet signatures.