Texture image retrieval based on statistical feature fusion
Hengbin Wang, Huaijing Qu · 2020
In view of the fact that multiple complementary feature representation can effectively improve the performance of image retrieval, this paper proposes a new texture image retrieval method based on statistical distribution feature fusion in dual-tree complex wavelet transform domain. Firstly, the statistical distribution energy of the coefficients is calculated in the low frequency subband. Then, in the high frequency complex subbands, the magnitude coefficients are modeled as the Weibull distribution and the relative phase coefficients are modeled as the von Mises distribution. Furthermore, the distribution energy and the estimated model parameters are fused into new features. Finally, the similarity measurement adopting optimal weighted sum is used to retrieve the texture images in the VisTex database. The experimental results show that, compared with the existing texture image retrieval approaches, the proposed method has a higher average retrieval rate.