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  • Abstract - Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation, and Recognition Using Knowledge Propagation
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Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation, and Recognition Using Knowledge Propagation
October 2009 (vol. 31 no. 10)
pp. 1747-1761
ASCII Text
x 
 
Yuanhao Chen, Long (Leo) Zhu, Alan Yuille, Hongjiang Zhang, "Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation, and Recognition Using Knowledge Propagation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 10, pp. 1747-1761, October, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2009.95,
author = {Yuanhao Chen and Long (Leo) Zhu and Alan Yuille and Hongjiang Zhang},
title = {Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation, and Recognition Using Knowledge Propagation},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {31},
number = {10},
issn = {0162-8828},
year = {2009},
pages = {1747-1761},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.95},
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 - Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation, and Recognition Using Knowledge Propagation
IS - 10
SN - 0162-8828
SP1747
EP1761
EPD - 1747-1761
A1 - Yuanhao Chen,
A1 - Long (Leo) Zhu,
A1 - Alan Yuille,
A1 - Hongjiang Zhang,
PY - 2009
KW - Unsupervised learning
KW - object classification
KW - segmentation
KW - recognition.
VL - 31
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Yuanhao Chen, University of Science and Technology of China, Hefei
Long (Leo) Zhu, Massachusetts Institute of Technology, Cambridge
Alan Yuille, University of California, Los Angeles, Los Angeles
Hongjiang Zhang, Microsoft Advanced Technology Center, Beijing
We present a method to learn probabilistic object models (POMs) with minimal supervision, which exploit different visual cues and perform tasks such as classification, segmentation, and recognition. We formulate this as a structure induction and learning task and our strategy is to learn and combine elementary POMs that make use of complementary image cues. We describe a novel structure induction procedure, which uses knowledge propagation to enable POMs to provide information to other POMs and “teach them” (which greatly reduces the amount of supervision required for training and speeds up the inference). In particular, we learn a POM-IP defined on Interest Points using weak supervision [1], [2] and use this to train a POM-mask, defined on regional features, which yields a combined POM that performs segmentation/localization. This combined model can be used to train POM-edgelets, defined on edgelets, which gives a full POM with improved performance on classification. We give detailed experimental analysis on large data sets for classification and segmentation with comparison to other methods. Inference takes five seconds while learning takes approximately four hours. In addition, we show that the full POM is invariant to scale and rotation of the object (for learning and inference) and can learn hybrid objects classes (i.e., when there are several objects and the identity of the object in each image is unknown). Finally, we show that POMs can be used to match between different objects of the same category, and hence, enable objects recognition.

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Index Terms:
Unsupervised learning, object classification, segmentation, recognition.
Citation:
Yuanhao Chen, Long (Leo) Zhu, Alan Yuille, Hongjiang Zhang, "Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation, and Recognition Using Knowledge Propagation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 10, pp. 1747-1761, Oct. 2009, doi:10.1109/TPAMI.2009.95
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