Image retrieval based on intrinsic spectral histogram representation

The spectral histogram features are not invariant to images´ scale transformation. We investigate in the technique of scale-invariant feature extraction. An approach is proposed to get the characteristic scales based on the reliable keypoints which are detected as local extrema in combination of normalized derivatives. Making use of characteristic scale of image content, which reflects characteristic length of a corresponding image structure, we are able to contribute in eliminating the effect of image transformation. In our content based image retrieval process, images are firstly resized by the characteristic scale and then represented based on the statistics of their spectral components and a linear dimension reduction technique optimizing class differentiation with respect to cross-correlation metrics of spectral histograms. Our retrieval consists of a preliminary classification step to index images in dataset and a following step of class by class retrieval. Experiments are performed on the Corel database and the outcome is compared with those of some existing work.