Many tasks in computer vision involve assigning a label (such as disparity) to every pixel. A
common constraint is that the labels should vary smoothly almost everywhere while …

In previous work on point matching, a set of points is often treated as an instance of a joint
distribution to exploit global relationships in the point set. For nonrigid shapes, however, the …

It is well-known that the problem of matching two relational structures can be posed as an
equivalent problem of finding a maximal clique in a (derived)" association graph." However …

Hypergraph clustering refers to the process of extracting maximally coherent groups from a
set of objects using high-order (rather than pairwise) similarities. Traditional approaches to …

We develop a framework for the image segmentation problem based on a new graph-
theoretic formulation of clustering. The approach is motivated by the analogies between the …

Recently, there are various types of display systems that can present aural, visual and haptic
information related to the user's position. It is also important to present olfactory information …

We present a new energy-minimization framework for the graph isomorphism problem which
is based on an equivalent maximum clique formulation. The approach is centered around a …

Deep metric learning has yielded impressive results in tasks such as clustering and image
retrieval by leveraging neural networks to obtain highly discriminative feature embeddings …

This article presents a new model for word sense disambiguation formulated in terms of
evolutionary game theory, where each word to be disambiguated is represented as a node …

Deep metric learning has yielded impressive results in tasks such as clustering and image
retrieval by leveraging neural networks to obtain highly discriminative feature embeddings …