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Unsupervised Image Categorization by Hypergraph Partition
June 2011 (vol. 33 no. 6)
pp. 1266-1273
ASCII Text
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Yuchi Huang, Qingshan Liu, Fengjun Lv, Yihong Gong, Dimitris N. Metaxas, "Unsupervised Image Categorization by Hypergraph Partition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 6, pp. 1266-1273, June, 2011.
 
 
BibTex
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@article{ 10.1109/TPAMI.2011.25,
author = {Yuchi Huang and Qingshan Liu and Fengjun Lv and Yihong Gong and Dimitris N. Metaxas},
title = {Unsupervised Image Categorization by Hypergraph Partition},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {33},
number = {6},
issn = {0162-8828},
year = {2011},
pages = {1266-1273},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.25},
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 Image Categorization by Hypergraph Partition
IS - 6
SN - 0162-8828
SP1266
EP1273
EPD - 1266-1273
A1 - Yuchi Huang,
A1 - Qingshan Liu,
A1 - Fengjun Lv,
A1 - Yihong Gong,
A1 - Dimitris N. Metaxas,
PY - 2011
KW - Unsupervised image categorization
KW - hypergraph
KW - hypergraph partition.
VL - 33
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Yuchi Huang, Rutgers University at New Brunswick, Piscataway
Qingshan Liu, Rutgers University at New Brunswick, Piscataway
Fengjun Lv, NEC Laboratories America, Inc, Cupertino
Yihong Gong, NEC Laboratories America, Inc., Cupertino
Dimitris N. Metaxas, Rutgers University at New Brunswick, Piscataway
We present a framework for unsupervised image categorization in which images containing specific objects are taken as vertices in a hypergraph and the task of image clustering is formulated as the problem of hypergraph partition. First, a novel method is proposed to select the region of interest (ROI) of each image, and then hyperedges are constructed based on shape and appearance features extracted from the ROIs. Each vertex (image) and its k-nearest neighbors (based on shape or appearance descriptors) form two kinds of hyperedges. The weight of a hyperedge is computed as the sum of the pairwise affinities within the hyperedge. Through all of the hyperedges, not only the local grouping relationships among the images are described, but also the merits of the shape and appearance characteristics are integrated together to enhance the clustering performance. Finally, a generalized spectral clustering technique is used to solve the hypergraph partition problem. We compare the proposed method to several methods and its effectiveness is demonstrated by extensive experiments on three image databases.

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Index Terms:
Unsupervised image categorization, hypergraph, hypergraph partition.
Citation:
Yuchi Huang, Qingshan Liu, Fengjun Lv, Yihong Gong, Dimitris N. Metaxas, "Unsupervised Image Categorization by Hypergraph Partition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 6, pp. 1266-1273, June 2011, doi:10.1109/TPAMI.2011.25
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