Calibrated Rank-SVM for multi-label image categorization

In the area of multi-label image categorization, there are two important issues: label classification and label ranking. The former refers to whether a label is relevant or not, and the latter refers to what extent a label is relevant to an image. However, few existing papers have considered them in a holistic way. In this paper we will suggest a concrete improved method, named calibrated RankSVM, to bridge the gap between multi-label classification and label ranking. Through incorporating a virtual label as a calibrated scale, the threshold selection stage is embedded into ranking learning stage. This holistic way is essentially different from conventional rank methods, making our proposed method more suitable for multi-label classification task. The experiments on image have demonstrated that our algorithm has better multi-label classification performances than conventional RankSVM while preserving its good ranking characteristics.