Non-linear dimensionality reductionmethods are powerful techniques to deal with
high-dimensional datasets. However, they often are susceptible to local minima
and perform poorly ...
Andreas Geiger (Karlsruhe Institute of Technology)...
In this paper, we introduce a novel algorithm to solve
global shape registration problems. We use gray-scale “images”
to represent source shapes, and propose a novel twocompo...
Hongsheng Li (Lehigh University), Tian Shen (Lehig...
Feature selection plays a fundamental role in many pattern
recognition problems. However, most efforts have been
focused on the supervised scenario, while unsupervised feature
s...
Bin Zhao, James Tin-Yau Kwok, Fei Wang, Changshui ...
Graph matching is an important problem in computer
vision. It is used in 2D and 3D object matching and recognition.
Despite its importance, there is little literature on
learnin...
Multi-instance multi-label learning (MIML) refers to the
learning problems where each example is represented by a
bag/collection of instances and is labeled by multiple labels.
...
Rong Jin (Michigan State University), Shijun Wang...
We present a new method for classification with structured
latent variables. Our model is formulated using the
max-margin formalism in the discriminative learning literature.
We...
Markov random field (MRF, CRF) models are popular in
computer vision. However, in order to be computationally
tractable they are limited to incorporate only local interactions
a...
We consider regions of images that exhibit smooth statistics, and pose the question of characterizing
the “essence” of these regions that matters for visual recognition. Ideal...
Ganesh Sundaramoorthi (UCLA), Peter Petersen (UCLA...
Accurately identifying corresponded landmarks from a
population of shape instances is the major challenge in
constructing statistical shape models. In general, shapecorrespondenc...
The aim of this work is to learn a shape prior model
for an object class and to improve shape matching with the
learned shape prior. Given images of example instances,
we can le...