Data-specific concept correlation estimation for video annotation refinement

For video annotation refinement, a reasonable concept correlation representation is crucial. In this paper, we present a data-specific concept correlation estimation procedure for this task, where the resulting correlation with respect to each data encodes both its visual and high-level characteristics. Specifically, this procedure comprises two major modules: concept correlation basis estimation and data-specific concept correlation calculation. Under the framework of sparse representation, the former introduces a set of high-level concept correlation bases to represent the concept distribution of each feature-level basis, while the latter constructs the concept correlation of a specific data by combining its feature-level sparse coefficients and correlation bases together. In the end, given this new correlation, a probability-calculation based video annotation refinement is performed on TRECVID 2006 dataset. The experiments show that such a representation capturing data-specific characteristics could achieve better performance, than the generic concept correlation applied to all data.