Image annotation by semi-supervised cross-domain learning with group sparsity

With the explosive growth of multimedia data in the web, multi-label image annotation has been attracted more and more attention. Although the amount of available data is large and growing, the number of labeled data is quite small. This paper proposes an approach to utilize both unlabeled data in target domain and labeled data in auxiliary domain to boost the performance of image annotation. Moreover, since different kinds of heterogeneous features in images have different intrinsic discriminative power for image understanding, group sparsity is introduced in our approach to effectively utilize those heterogeneous visual features with data of target and auxiliary domains. We call this approach semi-supervised cross-domain learning with group sparsity (S2CLGS). The strength of the proposed S2CLGS method for multi-label image annotation is to integrate semi-supervised discriminant analysis, cross-domain learning and sparse coding together. Experiments demonstrate the effectiveness of S2CLGS in comparison with other image annotation algorithms.

► We utilize both unlabeled data in target domain and labeled data in auxiliary domain for image annotation. ► Group sparsity is introduced to effectively utilize heterogeneous visual features. ► The proposed S2CLGS method integrates semi-supervised discriminant analysis, cross-domain learning and sparse coding together. ► Experiments demonstrate the effectiveness of our proposed method.