Multi-label classification with clustering for image and text categorization

This paper explores effective multi-label classification methods for multi-semantic image and text categorization. We perform an experimental study of clustering based multi-label classification (CBMLC) for the target problem. Experimental evaluation is conducted for identifying the impact of different clustering algorithms and base classifiers on the predictive performance and efficiency of CBMLC. In the experimental setting, three widely used clustering algorithms and six popular multi-label classification algorithms are used and evaluated on multi-label image and text datasets. A multi-label classification evaluation metrics, micro F1-measure, is used for presenting predictive performances of the classifications. Experimental evaluation results reveal that clustering based multi-label learning algorithms are more effective compared to their non-clustering counterparts.

I. INTRODUCTION

Along with rapid development of contemporary information technology, image and text in multimedia become key elements in effective communication and interaction in people's daily life. Accordingly, categorization of image and text has gained significant interest in recent years. However, It has been challenging for the categorization, particularly when a text document or an image instance is associated with multiple concepts [1]–[14].