We propose an unsupervised method for evaluating image segmentation. Common methods are typically based on evaluating smoothness within segments and contrast between them, and the...
We study a generative model in which hidden causes combine competitively to produce observations. Multiple active causes combine to determine the value of an observed variable thr...
We present a new Gaussian Process inference algorithm, called Online Sparse Matrix Gaussian Processes (OSMGP), and demonstrate its merits with a few vision applications. The OSMGP ...
Although very widely used in unsupervised data mining, most clustering methods are affected by the instability of the resulting clusters w.r.t. the initialization of the algorithm ...
We present a Bayesian search algorithm for learning the structure of latent variable models of continuous variables. We stress the importance of applying search operators designed...