Sign in
Author

Conference

Journal

Organization

Year

DOI
Look for results that meet for the following criteria:
since
equal to
before
between
and
Search in all fields of study
Limit my searches in the following fields of study
Agriculture Science
Arts & Humanities
Biology
Chemistry
Computer Science
Economics & Business
Engineering
Environmental Sciences
Geosciences
Material Science
Mathematics
Medicine
Physics
Social Science
Multidisciplinary
Keywords
(11)
Computer Vision
Conditional Random Field
Convex Optimization
Graph Cut
Iterative Solution
Learning Methods
Optimization Problem
Partition Function
Structure Methods
Structure Learning
Be Lief Propagation
Subscribe
Academic
Publications
Efficient piecewise learning for conditional random fields
Efficient piecewise learning for conditional random fields,10.1109/CVPR.2010.5540123,Karteek Alahari,Christopher Russell,Philip H. S. Torr
Edit
Efficient piecewise learning for conditional random fields
(
Citations: 3
)
BibTex

RIS

RefWorks
Download
Karteek Alahari
,
Christopher Russell
,
Philip H. S. Torr
Conditional Random Field
models have proved effec tive for several lowlevel
computer vision
problems. Infer ence in these models involves solving a combinatorial op timization problem, with methods such as graph cuts, be lief propagation. Although several methods have been pro posed to learn the model parameters from training data, they suffer from various drawbacks. Learning these pa rameters involves computing the partition function, which is intractable. To overcome this, stateoftheart structured
learning methods
frame the problem as one of large mar gin estimation. Iterative solutions have been proposed to solve the resulting
convex optimization
problem. Each iter ation involves solving an inference problem over all the la bels, which limits the efficiency of these structured methods. In this paper we present an efficient large margin piece wise learning method which is widely applicable. We show how the resulting
optimization problem
can be reduced to an equivalent convex problem with a small number of con straints, and solve it using an efficient scheme. Our method is both memory and computationally efficient. We show re sults on publicly available standard datasets.
Conference:
Computer Vision and Pattern Recognition  CVPR
, pp. 895901, 2010
DOI:
10.1109/CVPR.2010.5540123
Cumulative
Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
(
dx.doi.org
)
(
www.informatik.unitrier.de
)
(
cms.brookes.ac.uk
)
(
ieeexplore.ieee.org
)
(
ieeexplore.ieee.org
)
More »
Citation Context
(3)
...[
1
]). In this work, we just tune c empirically...
...Firstly, we measured the distribution of interior angles of triangles [28], to help assess the quality of the final mesh—ideally, interior angles should be close to 60 . Secondly, we computed the average distortion metric [29] for each triangle, defined as its area divided by the sum of the squares of the lengths of its edges and then normalised by a factor 2 p 3. The value of the distortion metric lies in [0,
1
]...
Ran Song
,
et al.
Higher Order CRF for Surface Reconstruction from Multiview Data Sets
...For instance, most of these methods impose restrictions on the type of the MRF potential functions that can be used during learning, and/or can handle only pairwise MRFs [3, 9, 14, 15, 11, 10,
2
]...
Nikos Komodakis
.
Efficient training for pairwise or higher order CRFs via dual decompos...
...∂wn . However, exact computation of these gradients is infeasible, because it would require vast computational effort to calculate the gradient marginalization of the partition function Z = i∈V Zi. Consequently, an efficient piecewise training technique [
16
] is adopted for parameter learning...
...More details of the piecewise training technique can be found in [
16
]...
Xi Li
,
et al.
Superpixelbased object class segmentation using conditional random fi...
References
(25)
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
(
Citations: 99
)
Dragomir Anguelov
,
Benjamin Taskar
,
Vassil Chatalbashev
,
Daphne Koller
,
Dinkar Gupta
,
Geremy Heitz
,
Andrew Y. Ng
Conference:
Computer Vision and Pattern Recognition  CVPR
, vol. 2, pp. 169176, 2005
Statistical analysis of nonlattice data
(
Citations: 422
)
J. Besag
Published in 1975.
An Experimental Comparison of MinCut/MaxFlow Algorithms for Energy Minimization in Vision
(
Citations: 797
)
Yuri Boykov
,
Vladimir Kolmogorov
Journal:
IEEE Transactions on Pattern Analysis and Machine Intelligence  PAMI
, vol. 26, no. 9, pp. 11241137, 2004
Efficient Belief Propagation for Early Vision
(
Citations: 369
)
Pedro F. Felzenszwalb
,
Daniel P. Huttenlocher
Conference:
Computer Vision and Pattern Recognition  CVPR
, vol. 1, pp. 261268, 2004
A Discriminatively Trained, Multiscale, Deformable Part Model
(
Citations: 215
)
Pedro F. Felzenszwalb
,
David A. Mcallester
,
Deva Ramanan
Conference:
Computer Vision and Pattern Recognition  CVPR
, pp. 18, 2008
Sort by:
Citations
(3)
Higher Order CRF for Surface Reconstruction from Multiview Data Sets
Ran Song
,
Yonghuai Liu
,
Ralph R. Martin
,
Paul L. Rosin
Conference:
International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission  3DIMPVT
, 2011
Efficient training for pairwise or higher order CRFs via dual decomposition
Nikos Komodakis
Conference:
Computer Vision and Pattern Recognition  CVPR
, pp. 18411848, 2011
Superpixelbased object class segmentation using conditional random fields
Xi Li
,
Hichem Sahbi
Conference:
International Conference on Acoustics, Speech, and Signal Processing  ICASSP
, pp. 11011104, 2011