Rotation Invariant Texture Retrieval Considering the Scale Dependence of Gabor Wavelet

Rotation Invariant Texture Retrieval Considering
the Scale Dependence of Gabor Wavelet

Abstract— Rotation Invariant Texture Retrieval Considering the Scale Dependence of Gabor Wavelet. 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 functionto capture the scale dependence of GW/CSGW for improving the retrieval performance. It is well known that the Kullback–Leibler distance < Final Year projects 2016 > 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.