Rotation Invariant Texture Retrieval Considering the Scale Dependence of Gabor Wavelet

Abstract

Obtaining robust and efficient rotation-invariant texture features in content-based image retrieval field is a challenging work. We propose three efficient rotation-invariant methods for texture image retrieval using copula model based in the domains of Gabor wavelet (GW) and circularly symmetric GW (CSGW). The proposed copula models use copula function to capture the scale dependence of GW/CSGW for improving the retrieval performance. It is well known that the Kullback-Leibler distance (KLD) is the commonly used similarity measurement between probability models. However, it is difficult to deduce the closed-form of KLD between two copula models due to the complexity of the copula model. We also put forward a kind of retrieval scheme using the KLDs of marginal distributions and the KLD of copula function to calculate the KLD of copula model. The proposed texture retrieval method has low computational complexity and high retrieval precision. The experimental results on VisTex and Brodatz data sets show that the proposed retrieval method is more effective compared with the state-of-the-art methods.