Abstract. We present a comparative study on how to use discriminative learning methods such as classification, regression, and ranking to address deformable shape segmentation. Tra...
Jingdan Zhang, Shaohua Kevin Zhou, Dorin Comaniciu...
The main theme of this paper is to develop a novel eigenvalue optimization framework for learning a Mahalanobis metric. Within this context, we introduce a novel metric learning a...
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Clustering algorithms have become increasingly important in handling and analyzing data. Considerable work has been done in devising effective but increasingly specific clustering...
Annaka Kalton, Pat Langley, Kiri Wagstaff, Jungsoo...
We introduce a non-linear shape prior for the deformable model framework that we learn from a set of shape samples using recent manifold learning techniques. We model a category o...