Max-margin multi-scale convolutional factor analysis model with application to image classification

Abstract

This paper presents a max-margin multi-scale convolutional factor analysis (MMCFA) model, which explores the strongly discriminative principle of max-margin learning to improve the classification performance of multi-scale convolutional factor analysis (CFA) model with application to image data classification. Compared with the traditional factor analysis (FA) model, the CFA model can maintain the spatial correlation among the image pixels in two-dimensional space and capture the structural information from images via convolution kernels. Moreover, to extract multi-level features, multi-scale convolution kernels are adopted to capture richer features at different scales of images. Since the unsupervised model may not offer discriminative factors for the classification task, it is expected to introduce the supervised information to the multi-scale CFA model when supervised information is available. To deal with it, a latent variable support vector machine (LVSVM) is linked to the factors learned from multi-scale CFA model, yielding max-margin discrimination, as the classification criterion in the feature space in our proposed model. The multi-scale CFA model and LVSVM learn parameters jointly in a united framework via the Gibbs inference. Experimental results on mixed national institute of standards and technology (MNIST) dataset, Fashion-MNIST dataset, SVHN dataset and measured synthetic aperture radar (SAR) images show that the learned convolution kernels and factors can describe data information well and the proposed model has excellent classification performance.