Texture is an important visual property that characterizes a wide range of natural and artificial images which makes it a useful feature for retrieving images. Several approaches have been proposed to describe the texture contents of an image. In early research works, such as edge histograms-based techniques and co-occurrence-based approaches, texture descriptors were mainly extracted from the spatial domain. Later on, dual spaces (transform of spatial domain) such as frequency space or spaces resulting from Gabor or wavelet transforms were explored for texture characterization. Recent physiological studies showed that human visual system can be modeled as a set of independent channels of various orientations and scales, this finding motivated the proliferation of multi-resolution methods for describing texture images. Most of these methods are either wavelet-based or Gabor-based. This paper summarizes our recent study of the use of Fourier-based techniques for characterizing image textures. At first, a single-resolution Fourier-based technique is proposed and its performance is compared against the performance of some classical Fourier-based methods. The proposed technique is then extended into a multi-resolution version. Performance of the modified technique is compared against those of the single-resolution approach and some other multi-resolution approaches recently described in literature. Two performance indicators were used in this comparison: retrieval accuracy and execution time of the techniques.