We consider the problem of learning with instances defined over a space of sets of vectors. We derive a new positive definite kernel f(A B) defined over pairs of matrices A B base...
Non-linear subspaces derived using kernel methods have been found to be superior compared to linear subspaces in modeling or classification tasks of several visual phenomena. Such...
Most modern computer vision systems for high-level
tasks, such as image classification, object recognition and
segmentation, are based on learning algorithms that are
able to se...
Scale-space representation of an image is a significant way to generate features for classification. However, for a specific classification task, the entire scale-space may not be...
Recently SVMs using spatial pyramid matching (SPM)
kernel have been highly successful in image classification.
Despite its popularity, these nonlinear SVMs have a complexity
O(n...
Jianchao Yang, Kai Yu, Yihong Gong, Thomas S. Huan...