Cross-Domain Web Image Annotation

In recent years, cross-domain learning algorithms have attracted much attention to solve labeled data insufficient problem. However, these cross-domain learning algorithms cannot be applied for subspace learning, which plays a key role in multimedia, e. g., Web image annotation. This paper envisions the cross-domain discriminative subspace learning and provides an effective solution to cross-domain subspace learning. In particular, we propose the cross-domain discriminative Hessian eigenmaps or CDHE for short. CDHE connects the training and the testing samples by minimizing the quadratic distance between the distribution of the training samples and that of the testing samples. Therefore, a common subspace for data representation can be preserved. We basically expect the discriminative information to separate the concepts in the training set can be shared to separate the concepts in the testing set as well and thus we have a chance to address above cross-domain problem duly. The margin maximization is duly adopted in CDHE so the discriminative information for separating different classes can be well preserved. Finally, CDHE encodes the local geometry of each training class in the local tangent space which is locally isometric to the data manifold and thus can locally preserve the intra-class local geometry. Experimental evidence on real world image datasets demonstrates the effectiveness of CDHE for cross-domain Web image annotation.