A robust wavelet domain multi-scale texture descriptor for image classification

Local binary pattern (LBP) and many variants adopt the difference information to encode all the neighborhood binarization, demonstrating excellent ability to distinguish images. However, these methods are very sensitive to reverse gray changes. How to improve the robustness of reverse gray changes and maintain excellent classification ability has become a problem to be solved. In this paper, an image captioning method called multiscale image captioning method based on wavelet transform is proposed (WMLBP). This method uses stationary wavelet transform to remove the redundant information of the image and extract features from the frequency domain. This paper also proposes an innovative coding scheme, which designs three descriptors, and uses multi- scale hierarchical thresholds and complementary information to enhance the ability of image feature description. Finally, the image features were described by combining all the descriptors and using the multi-scale and multi- resolution cross-scale joint representation. Tests across multiple databases have demonstrated that the preprocessing of this method can significantly enhance classification capabilities. Compared to other algorithms, it has a higher accuracy rate and faster computational speed. Experiments demonstrate that the method exhibits strong robustness against grayscale inversion changes (both linear and nonlinear) and image rotation, while also demonstrating excellent classification performance. It holds significant importance for addressing the challenges encountered in image classification. Our code is available at: https://github.com/Dawei-W/WMLBP/tree/master .