  The advent of large scale multimedia databases has led to great challenges in
content-based image retrieval (CBIR). Even though CBIR is considered an
emerging field of research, however it constitutes a strong background for new
methodologies and systems implementations. Therefore, many research
contributions are focusing on techniques enabling higher image retrieval
accuracy while preserving low level of computational complexity. Image
retrieval based on texture features is receiving special attention because of
the omnipresence of this visual feature in most real-world images. This paper
highlights the state-of-the-art and current progress relevant to texture-based
image retrieval and spatial-frequency image representations. In particular, it
gives an overview of statistical methodologies and techniques employed for
texture feature extraction using most popular spatial-frequency image
transforms, namely discrete wavelets, Gabor wavelets, dual-tree complex wavelet
and contourlets. Indications are also given about used similarity measurement
functions and most important achieved results.
