Texture image retrieval using novel non-separable filter banks based on centrally symmetric matrices

Though millions of images are stored in a large digital image library today, the user can not access or make full use of these image information unless the digital image library is well organized in order to allow efficient browsing, searching and retrieval. Thus, research in image retrieval has been an active discipline since 70's last century. Image retrieval is a typical problem of pattern recognition, consisting of two parts: extracting features (EF) and similarity measurement (SM). In this paper, we develop new non-separable filter banks based on the centrally symmetric matrixes, and apply them to extract the features of texture images. Compared to tensor product wavelets, our new filter banks can capture more directional texture information, which is helpful for texture image retrieval. Experiments show that our novel non-separable filter banks are satisfiable and achieve a better retrieval effectiveness than Daubechies wavelets.