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» Invariances in kernel methods: From samples to objects
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CORR
2011
Springer
182views Education» more  CORR 2011»
12 years 11 months ago
Adaptively Learning the Crowd Kernel
We introduce an algorithm that, given n objects, learns a similarity matrix over all n2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen t...
Omer Tamuz, Ce Liu, Serge Belongie, Ohad Shamir, A...
ICASSP
2009
IEEE
14 years 2 months ago
Sampling signals with finite rate of innovation in the presence of noise
Recently, it has been shown that it is possible to sample non-bandlimited signals that possess a limited number of degrees of freedom and uniquely reconstruct them from a finite ...
Pier Luigi Dragotti, Felix Homann
ICCV
2005
IEEE
14 years 1 months ago
Fast Global Kernel Density Mode Seeking with Application to Localisation and Tracking
We address the problem of seeking the global mode of a density function using the mean shift algorithm. Mean shift, like other gradient ascent optimisation methods, is susceptible...
Chunhua Shen, Michael J. Brooks, Anton van den Hen...
ICCV
1995
IEEE
13 years 11 months ago
A Multi-Body Factorization Method for Motion Analysis
The structure-from-motion problem has been extensively studied in the field of computer vision. Yet, the bulk of the existing work assumes that the scene contains only a single m...
João Paulo Costeira, Takeo Kanade
ICIP
2008
IEEE
14 years 9 months ago
Activity-based temporal segmentation for videos of interacting objects using invariant trajectory features
This paper presents a content-based approach for temporal segmentation of videos. Tracked objects are characterized by their 2D trajectories which are used in a meaningful way to ...
Alexandre Hervieu, Patrick Bouthemy, Jean-Pierre L...