Multi-label classification for images with missing labels

Abstract

Multi-label classification is a vital problem, as it has numerous applications in computer vision, such as automatic image annotation. The label set for each instance is always assumed to be in the original whole form. However, missing labels often occur because manual labelling is a time-consuming and label-intensive work in the case of large amount of data. The incompleteness of labels can certainly increase the difficulty of training the multi-label model. In this paper, a novel multi-label classification method is proposed that can learn the inductive classifier while explicitly dealing with missing labels. An individual sparsity inducing l1-norm is employed to capture the sparse label interdependencies. A group sparsity inducing l2,1-norm is utilized to select the discriminative input features. The semantic label hierarchy is included to diversify the label dependency. Meanwhile, the consistency between the predicted labels and the original labels as well as the regularization of smoothness on the predicted labels are also enforced to improve the classification performance. Furthermore, an efficient method based on the alternating direction method of multipliers is designed to facilitate classifier and label correlation learning process. Experiments on two widely used large-scale image datasets demonstrate that the efficacy of the proposed method on multi-label classification when only a limited number of labels are given for each training sample.