Discrete semi-supervised learning for multi-label image classification and large-scale image retrieval

Abstract

Multi-label image classification is a critical problem in image semantic learning. Traditional semi-supervised multi-label learning methods are mainly based on continuous learning of both labelled and unlabelled data. They usually learn classification functions from continuous label space. And the neglect of discrete constraint of labels impedes the classification performance. In this paper, we specifically consider the discrete constraint and propose Discrete Semi-supervised Multi-label Learning (DSML) for image classification. In DSML, we propose a semi-supervised framework with discrete constraint. Then we introduce anchor graph learning to improve the scalability, and derive an ADMM based alternating optimization process to solve our framework. The main experimental results on two real-world image datasets MIR Flickr and NUS-WIDE demonstrate the superiority of DSML compared with several advanced multi-label methods. Furthermore, additional experiments of image retrieval show the potential advantages of DSML in other image applications.