Graph is a popular data representation capturing relations among samples, such as images and documents. Many successful graph-based techniques, such as Regularized Laplacian and Random Walk, have been used for multimedia applications in retrieval and classification. In this talk, I will review a few novel graph-based techniques designed specifically to handle the new challenges associated with large-scale noisy multimedia data encountered on the Web. I will review (1) label diagnosis and spectral filtering techniques for removing unreliable labels, (2) anchor graph methods for scaling up graph-based techniques to gigantic data sets, and (3) multi-edge graph that captures heterogeneous similarities among multimedia data. Applications in Web multimedia retrieval and novel systems searching images with Brain Machine Interfaces will be presented.
Back to Large Scale Multimedia Search