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OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues
March 2010 (vol. 32 no. 3)
pp. 530-545
ASCII Text
x 
 
M. Pawan Kumar, P.H.S. Torr, A. Zisserman, "OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 3, pp. 530-545, March, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2009.16,
author = {M. Pawan Kumar and P.H.S. Torr and A. Zisserman},
title = {OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {32},
number = {3},
issn = {0162-8828},
year = {2010},
pages = {530-545},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.16},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues
IS - 3
SN - 0162-8828
SP530
EP545
EPD - 530-545
A1 - M. Pawan Kumar,
A1 - P.H.S. Torr,
A1 - A. Zisserman,
PY - 2010
KW - Object category specific segmentation
KW - conditional random fields
KW - generalized EM
KW - graph cuts.
VL - 32
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
M. Pawan Kumar, Stanford University, Stanford
P.H.S. Torr, Oxford Brookes University, Oxford
A. Zisserman, University of Oxford, Oxford
We present a probabilistic method for segmenting instances of a particular object category within an image. Our approach overcomes the deficiencies of previous segmentation techniques based on traditional grid conditional random fields (CRF), namely that 1) they require the user to provide seed pixels for the foreground and the background and 2) they provide a poor prior for specific shapes due to the small neighborhood size of grid CRF. Specifically, we automatically obtain the pose of the object in a given image instead of relying on manual interaction. Furthermore, we employ a probabilistic model which includes shape potentials for the object to incorporate top-down information that is global across the image, in addition to the grid clique potentials which provide the bottom-up information used in previous approaches. The shape potentials are provided by the pose of the object obtained using an object category model. We represent articulated object categories using a novel layered pictorial structures model. Nonarticulated object categories are modeled using a set of exemplars. These object category models have the advantage that they can handle large intraclass shape, appearance, and spatial variation. We develop an efficient method, OBJCUT, to obtain segmentations using our probabilistic framework. Novel aspects of this method include: 1) efficient algorithms for sampling the object category models of our choice and 2) the observation that a sampling-based approximation of the expected log-likelihood of the model can be increased by a single graph cut. Results are presented on several articulated (e.g., animals) and nonarticulated (e.g., fruits) object categories. We provide a favorable comparison of our method with the state of the art in object category specific image segmentation, specifically the methods of Leibe and Schiele and Schoenemann and Cremers.

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Index Terms:
Object category specific segmentation, conditional random fields, generalized EM, graph cuts.
Citation:
M. Pawan Kumar, P.H.S. Torr, A. Zisserman, "OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 3, pp. 530-545, March 2010, doi:10.1109/TPAMI.2009.16
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