Image Singularity Scattering Representation Learning Classification

The multi-scale geometric analysis is a great representation tool. It can be used to improve the feature representation and learning process of deep networks. In addition to extracting features, the multi-scale geometric prior knowledge can also be used for the structure improvement of deep networks. In this paper, we propose a multi-scale scattering representation learning network, abbreviated as MSRLN, for image classification tasks. The exploration of structure improvement can be made with multi-scale scattering operations. In this way, the better singularity representation learning process for networks can be achieved. Firstly, the filter banks and multi-scale scattering operator are introduced for non-linear and singularity representation. Secondly, the novel multi-scale scattering representation learning network structure is designed. The scaling- wise scattering process is deployed in the shallow layer as a non-linear layer. This structure essentially supplements deep networks with geometric prior knowledge. It can further improve the non-linear activation and singularity representation process. Thirdly, we put forward the multi-stage scattering representation strategy and the prior knowledge weakening mechanism. With flexible scaling factors and learning rates, the stepwise approximation and learning process of networks can be achieved. In sum, MSRLN is a kind of structural innovative, and the scattering singularity representation structure can be extended to other backbones or tasks. Extensive experimental results show that MSRLN can achieve better image classification accuracy. Finally, necessary convergence, insight, and adaptability analyses are provided in evaluation experiments.