Efficient multi-label ranking for multi-class learning: Application to object recognition

Abstract

Multi-label learning is useful in visual object recognition when several objects are present in an image. Conventional approaches implement multi-label learning as a set of binary classification problems, but they suffer from imbalanced data distributions when the number of classes is large. In this paper, we address multi-label learning with many classes via a ranking approach, termed multi-label ranking. Given a test image, the proposed scheme aims to order all the object classes such that the relevant classes are ranked higher than the irrelevant ones. We present an efficient algorithm for multi-label ranking based on the idea of block coordinate descent. The proposed algorithm is applied to visual object recognition. Empirical results on the PASCAL VOC 2006 and 2007 data sets show promising results in comparison to the state-of-the-art algorithms for multi-label learning.