Texture image retrieval based on statistical feature fusion

Hengbin Wang; Huaijing Qu

doi:10.1117/12.2557177

3 January 2020 Texture image retrieval based on statistical feature fusion

Hengbin Wang, Huaijing Qu

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113730Y (2020) https://doi.org/10.1117/12.2557177
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

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.

Citation Download Citation

Hengbin Wang and Huaijing Qu "Texture image retrieval based on statistical feature fusion", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113730Y (3 January 2020); https://doi.org/10.1117/12.2557177

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