Please use this identifier to cite or link to this item: https://doi.org/10.1109/TPAMI.2008.218
Title: Two-dimensional multilabel active learning with an efficient online adaptation model for image classification
Authors: Qi, G.-J.
Hua, X.-S.
Rui, Y.
Tang, J. 
Zhang, H.-J.
Keywords: Active learning
Image annotation
Multilabel classification
Online adaption
Issue Date: 2009
Source: Qi, G.-J., Hua, X.-S., Rui, Y., Tang, J., Zhang, H.-J. (2009). Two-dimensional multilabel active learning with an efficient online adaptation model for image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (10) : 1880-1897. ScholarBank@NUS Repository. https://doi.org/10.1109/TPAMI.2008.218
Abstract: Conventional active learning dynamically constructs the training set only along the sample dimension. While this is the right strategy in binary classification, it is suboptimal for multilabel image classification. We argue that for each selected sample, only some effective labels need to be annotated while others can be inferred by exploring the label correlations. The reason is that the contributions of different labels to minimizing the classification error are different due to the inherent label correlations. To this end, we propose to select sample-label pairs, rather than only samples, to minimize a multilabel Bayesian classification error bound. We call it two-dimensional active learning because it considers both the sample dimension and the label dimension. Furthermore, as the number of training samples increases rapidly over time due to active learning, it becomes intractable for the offline learner to retrain a new model on the whole training set. So we develop an efficient online learner to adapt the existing model with the new one by minimizing their model distance under a set of multilabel constraints. The effectiveness and efficiency of the proposed method are evaluated on two benchmark data sets and a realistic image collection from a real-world image sharing Web site - Corbis. © 2009 IEEE.
Source Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/38977
ISSN: 01628828
DOI: 10.1109/TPAMI.2008.218
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

44
checked on Dec 7, 2017

WEB OF SCIENCETM
Citations

30
checked on Nov 29, 2017

Page view(s)

54
checked on Dec 11, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.