TY - GEN
T1 - Uncertainty reduction for active image clustering via a hybrid global-local uncertainty model
AU - Xiong, Caiming
AU - Johnson, David M.
AU - Corso, Jason J.
PY - 2013
Y1 - 2013
N2 - We propose a novel combined global/local model for active semi-supervised spectral clustering based on the principle of uncertainty reduction. We iteratively compute the derivative of the eigenvectors produced by spectral decomposition with respect to each item/image, and combine this with local label entropy provided by the current clustering results in order to estimate the uncertainty reduction potential of each item in the dataset. We then generate pairwise queries with respect to the best candidate item and retrieve the needed constraints from the user. We evaluate our method using three different image datasets - faces, leaves and dogs, and consistently demonstrate performance superior to the current state-of-the-art.
AB - We propose a novel combined global/local model for active semi-supervised spectral clustering based on the principle of uncertainty reduction. We iteratively compute the derivative of the eigenvectors produced by spectral decomposition with respect to each item/image, and combine this with local label entropy provided by the current clustering results in order to estimate the uncertainty reduction potential of each item in the dataset. We then generate pairwise queries with respect to the best candidate item and retrieve the needed constraints from the user. We evaluate our method using three different image datasets - faces, leaves and dogs, and consistently demonstrate performance superior to the current state-of-the-art.
UR - http://www.scopus.com/inward/record.url?scp=84898855356&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84898855356&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84898855356
SN - 9781577356288
T3 - AAAI Workshop - Technical Report
SP - 149
EP - 151
BT - Late-Breaking Developments in the Field of Artificial Intelligence - Papers Presented at the 27th AAAI Conference on Artificial Intelligence, Technical Report
T2 - 27th AAAI Conference on Artificial Intelligence, AAAI 2013
Y2 - 14 July 2013 through 18 July 2013
ER -