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Layered Object Models for Image Segmentation
Sept. 2012 (vol. 34 no. 9)
pp. 1731-1743
ASCII Text
x 
 
Yi Yang, S. Hallman, D. Ramanan, C. C. Fowlkes, "Layered Object Models for Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 9, pp. 1731-1743, Sept., 2012.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2011.208,
author = { Yi Yang and S. Hallman and D. Ramanan and C. C. Fowlkes},
title = {Layered Object Models for Image Segmentation},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {34},
number = {9},
issn = {0162-8828},
year = {2012},
pages = {1731-1743},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.208},
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 - Layered Object Models for Image Segmentation
IS - 9
SN - 0162-8828
SP1731
EP1743
EPD - 1731-1743
A1 - Yi Yang,
A1 - S. Hallman,
A1 - D. Ramanan,
A1 - C. C. Fowlkes,
PY - 2012
KW - probability
KW - image segmentation
KW - object detection
KW - human segmentation
KW - layered object models
KW - image segmentation
KW - object detection
KW - generative probabilistic model
KW - object detectors
KW - shape masks
KW - appearance
KW - depth ordering
KW - image pixel labelling
KW - class label estimation
KW - object instance label estimation
KW - class segmentation
KW - instance segmentation
KW - PASCAL 2009 segmentation challenge data sets
KW - PASCAL 2010 segmentation challenge data sets
KW - Shape
KW - Image segmentation
KW - Image color analysis
KW - Detectors
KW - Object detection
KW - Mathematical model
KW - Computational modeling
KW - segmentation benchmark.
KW - Image segmentation
KW - multiclass object detection
KW - layered model
KW - 2.1D model
VL - 34
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Yi Yang, Dept. of Comput. Sci., Univ. of California at Irvine, Irvine, CA, USA
S. Hallman, Dept. of Comput. Sci., Univ. of California at Irvine, Irvine, CA, USA
D. Ramanan, Dept. of Comput. Sci., Univ. of California at Irvine, Irvine, CA, USA
C. C. Fowlkes, Dept. of Comput. Sci., Univ. of California at Irvine, Irvine, CA, USA
We formulate a layered model for object detection and image segmentation. We describe a generative probabilistic model that composites the output of a bank of object detectors in order to define shape masks and explain the appearance, depth ordering, and labels of all pixels in an image. Notably, our system estimates both class labels and object instance labels. Building on previous benchmark criteria for object detection and image segmentation, we define a novel score that evaluates both class and instance segmentation. We evaluate our system on the PASCAL 2009 and 2010 segmentation challenge data sets and show good test results with state-of-the-art performance in several categories, including segmenting humans.

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Index Terms:
probability,image segmentation,object detection,human segmentation,layered object models,image segmentation,object detection,generative probabilistic model,object detectors,shape masks,appearance,depth ordering,image pixel labelling,class label estimation,object instance label estimation,class segmentation,instance segmentation,PASCAL 2009 segmentation challenge data sets,PASCAL 2010 segmentation challenge data sets,Shape,Image segmentation,Image color analysis,Detectors,Object detection,Mathematical model,Computational modeling,segmentation benchmark.,Image segmentation,multiclass object detection,layered model,2.1D model
Citation:
Yi Yang, S. Hallman, D. Ramanan, C. C. Fowlkes, "Layered Object Models for Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 9, pp. 1731-1743, Sept. 2012, doi:10.1109/TPAMI.2011.208
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