Fast Texture Image Retrieval Using Learning Copula Model of Multiple DTCWT

In this work, we proposed a fast texture image retrieval method by using the learning Copula model of multiple Dual-tree complex wavelet transforms (DTCWTs). Compared with the discrete wavelet transform, DTCWT provides multiple-directions and multiple-scales decomposition to image and also has the fast calculation capability. In the proposed method Multiple DTCWTs is incorporated to get more texture features; compare to Gabor wavelet, DTCWT has less computational cost of decomposition. In DTCWT domains, we developed a Learning Copula Model (called LCMoMD) to describe the dependence between the subbands of multiple DTCWTs. For improving the retrieval performance, LCMoMD is first embedded in the linear space by utilizing matrix logarithm and Kernel Principal Component Analysis (KPCA) is used to calculate the features from the embedding Copula model in the linear space. Experiments demonstrate that our method has fast and robust performance of texture extraction compared to the state-of-the-art methods.