Image label completion by pursuing contextual decomposability

This article investigates how to automatically complete the missing labels for the partially annotated images, without image segmentation. The label completion procedure is formulated as a nonnegative data factorization problem, to decompose the global image representations that are used for describing the entire images, for instance, various image feature descriptors, into their corresponding label representations, that are used for describing the local semantic regions within images. The solution provided in this work is motivated by following observations. First, label representations of the regions with the same label often share certain commonness, yet may be essentially different due to the large intraclass variations. Thus, each label or concept should be represented by using a subspace spanned by an ensemble of basis, instead of a single one, to characterize the intralabel diversities. Second, the subspaces for different labels are different from each other. Third, while two images are similar with each other, the corresponding label representations should be similar. We formulate this cross-image context as well as the given partial label annotations in the framework of nonnegative data factorization and then propose an efficient multiplicative nonnegative update rules to alternately optimize the subspaces and the reconstruction coefficients. We also provide the theoretic proof of algorithmic convergence and correctness. Extensive experiments over several challenging image datasets clearly demonstrate the effectiveness of our proposed solution in boosting the quality of image label completion and image annotation accuracy. Based on the same formulation, we further develop a label ranking algorithms, to refine the noised image labels without any manual supervision. We compare the proposed label ranking algorithm with the state-of-the-arts over the popular evaluation databases and achieve encouragingly improvements.