Supervised learning of a parts-based model can be for-
mulated as an optimization problem with a large (exponen-
tial in the number of parts) set of constraints. We show how
thi...
M. Pawan Kumar, Andrew Zisserman, Philip H.S. Torr
We present a new technique (RIIPS) for solving rostering problems in the presence of service uncertainty. RIIPS stands for "Rostering by Iterating Integer Programming and Sim...
kernel canonical correlation analysis (KCCA) is a recently addressed supervised machine learning methods, which shows to be a powerful approach of extracting nonlinear features for...
Color is commonly used to represent categories and values in many computer applications, but differentiating these colors can be difficult in many situations (e.g., for users with...
Content-based image retrieval requires a natural image (e.g, a photo) as query, but the absence of such a query image is usually the reason for a search. An easy way to express the...