Publication
Regression-based techniques have shown promising results for people counting in crowded scenes. However, most existing techniques require expensive and laborious data annotation fo...
Publication
While clustering is usually an unsupervised operation, there are circumstances in which we have prior belief that pairs of samples should (or should not) be assigned to the same cl...
Publication
Existing person re-identification methods conventionally rely on labelled pairwise data to learn a task-specific distance metric for ranking. The value of unlabelled gallery instan...
ICCV
2011
IEEE
13 years 4 months ago
2011
IEEE
Human motion capturing (HMC) from multiview image sequences constitutes an extremely difficult problem due to depth and orientation ambiguities and the high dimensionality of the s...
ICASSP
2011
IEEE
13 years 4 months ago
2011
IEEE
In this paper, we consider the problem of manifold approximation with affine subspaces. Our objective is to discover a set of low dimensional affine subspaces that represents ma...
ICASSP
2011
IEEE
13 years 4 months ago
2011
IEEE
We start with a locally defined principal curve definition for a given probability density function (pdf) and define a pairwise manifold score based on local derivatives of the...
ICASSP
2011
IEEE
13 years 4 months ago
2011
IEEE
The construction of low-dimensional models explaining highdimensional signal observations provides concise and efficient data representations. In this paper, we focus on pattern ...
TIP
2010
13 years 7 months ago
2010
We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the ...
SIAMCO
2010
13 years 7 months ago
2010
Abstract. We establish results for the problem of tracking a time-dependent manifold arising in realtime optimization by casting this as a parametric generalized equation. We demon...
JMLR
2010
13 years 7 months ago
2010
We address instance-based learning from a perceptual organization standpoint and present methods for dimensionality estimation, manifold learning and function approximation. Under...