We introduce a ScatterNet that uses a parametric log transformation with Dual-Tree complex wavelets to extract translation invariant representations from a multi-resolution image. The parametric transformation aids the OLS pruning algorithm by converting the skewed distributions into relatively mean-symmetric distributions while the Dual-Tree wavelets improve the computational efficiency of the network. The proposed network is shown to outperform Mallat's ScatterNet on two image datasets, both for classification accuracy and computational efficiency. The advantages of the proposed network over other supervised and some unsupervised methods are also presented using experiments performed on different training dataset sizes.

本文介绍了一种基于双树复杂小波和参数化对数变换的ScatterNet，用于提取具有平移不变性的多分辨率图像表示。该方法通过将偏态分布转换成相对的均值对称分布，从而帮助OLS修剪算法。在两个图像数据集上，该网络的性能表现优于Mallat的ScatterNet，不仅在分类准确性方面，而且在计算效率方面也有所提高。本文还使用不同的训练数据集大小对该方法与其他监督和一些非监督方法进行了比较与实验，展现了该网络的优势。

基于双树小波散射网络和参数化对数变换的物体分类方法