Multiple-relations-constrained image classification with limited training samples via Pareto optimization

Abstract

It is a significant challenge to classify images using limited training samples. To this end, we formulate image classification as a multi-task multi-view (MTMV) learning problem and propose a novel Pareto optimization-based method to find the solution. Specifically, we first build a multi-objective multiple-relations-constrained MTMV model called M4 to formulate the procedure of image classification. This model integrates comprehensive relations so that more knowledge can be used when classifying the images. We formulate the model as a multi-objective optimization problem, which addresses conflicts between the inconsistencies of each item in the model and the limitation on the number of relations. To generate the final classifier for each image classification task, an effective Pareto optimization-based algorithm Pareto-M4 is proposed. Pareto-M4 first generates the Pareto-optimal solutions using a novel multi-objective solver MOQPSO and then obtains the final classifiers from all the Pareto-optimal solutions using a recombination procedure. Experiments on various real-world image data sets demonstrate the effectiveness of the proposed image classification method when limited training samples are given.