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Object Detection with Discriminatively Trained Part-Based Models
September 2010 (vol. 32 no. 9)
pp. 1627-1645
ASCII Text
x 
 
Pedro F. Felzenszwalb, Ross B. Girshick, David McAllester, Deva Ramanan, "Object Detection with Discriminatively Trained Part-Based Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627-1645, September, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2009.167,
author = {Pedro F. Felzenszwalb and Ross B. Girshick and David McAllester and Deva Ramanan},
title = {Object Detection with Discriminatively Trained Part-Based Models},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {32},
number = {9},
issn = {0162-8828},
year = {2010},
pages = {1627-1645},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.167},
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 - Object Detection with Discriminatively Trained Part-Based Models
IS - 9
SN - 0162-8828
SP1627
EP1645
EPD - 1627-1645
A1 - Pedro F. Felzenszwalb,
A1 - Ross B. Girshick,
A1 - David McAllester,
A1 - Deva Ramanan,
PY - 2010
KW - Object recognition
KW - deformable models
KW - pictorial structures
KW - discriminative training
KW - latent SVM.
VL - 32
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Pedro F. Felzenszwalb, University of Chicago, Chicago
Ross B. Girshick, University of Chicago, Chicago
David McAllester, Toyota Technological Institute at Chicago, Chicago
Deva Ramanan, University of California, Irvine, Irvine
We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL data sets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI--SVM in terms of latent variables. A latent SVM is semiconvex, and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function.

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Index Terms:
Object recognition, deformable models, pictorial structures, discriminative training, latent SVM.
Citation:
Pedro F. Felzenszwalb, Ross B. Girshick, David McAllester, Deva Ramanan, "Object Detection with Discriminatively Trained Part-Based Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627-1645, Sept. 2010, doi:10.1109/TPAMI.2009.167
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