Uncertainty reduction for active image clustering via a hybrid global-local uncertainty model

Caiming Xiong, David M. Johnson, Jason J. Corso

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationLate-Breaking Developments in the Field of Artificial Intelligence - Papers Presented at the 27th AAAI Conference on Artificial Intelligence, Technical Report
Pages149-151
Number of pages3
StatePublished - 2013
Event27th AAAI Conference on Artificial Intelligence, AAAI 2013 - Bellevue, WA, United States
Duration: 14 Jul 201318 Jul 2013

Publication series

NameAAAI Workshop - Technical Report
VolumeWS-13-17

Conference

Conference27th AAAI Conference on Artificial Intelligence, AAAI 2013
Country/TerritoryUnited States
CityBellevue, WA
Period14/07/1318/07/13

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