Bilinear Embedding Label Propagation: Towards Scalable Prediction of Image Labels

Traditional label propagation (LP) is shown to be effective for transductive classification. To enable the standard LP to handle outside images, two inductive methods by label reconstruction or by direct embedding have been presented, of which the latter scheme is relatively more efficient, especially for testing. But almost all inductive LP models use 1D vectors of images as inputs, which may destroy the topology structure of image pixels and usually suffer from high complexity due to the high dimension of 1D vectors in reality. To preserve the topology among pixels and address the scalability issue for the embedding based scheme, we propose a simple yet efficient Bilinear Embedding Label Propagation (BELP) by including a bilinear regularization term in terms of tensor representation to correlate the image labels with their bilinear features. BELP performs label prediction over the 2D matrices rather than 1D vectors, since images are essentially matrices. Finally, labels of new images can be easily obtained by embedding them onto a spanned bilinear subspace solved from a joint framework. Simulations verified the efficiency of our approach.