: This paper addresses the inference of probabilistic classification models using weakly supervised learning. The main contribution of this work is the development of learning meth...
We have developed a two-phase generative / discriminative learning procedure for the recognition of classes of objects and concepts in outdoor scenes. Our method uses both multipl...
This paper introduces a novel image decomposition approach for an ensemble of correlated images, using low-rank and sparsity constraints. Each image is decomposed as a combination...
We consider learning models for object recognition from examples. Our method is motivated by systems that use the Hausdorff distance as a shape comparison measure. Typically an ob...
Suppose a set of arbitrary (unlabeled) images contains frequent occurrences of 2D objects from an unknown category. This paper is aimed at simultaneously solving the following rel...