NDLI: Domain separation for efficient adaptive active learning

Content Provider	IEEE Xplore Digital Library
Author	Matasci, G. Tuia, D. Kanevski, M.
Copyright Year	2011
Description	Author affiliation: Institute of Geomatics and Analysis of Risk, University of Lausanne, Switzerland (Matasci, G.; Kanevski, M.) \|\| Image Processing Laboratory (IPL), Universitat de València, Spain (Tuia, D.)
Abstract	This paper proposes a procedure aimed at efficiently adapting a classifier trained on a source image to a similar target image. The adaptation is carried out through active queries in the target domain following a strategy particularly designed for the case where class distributions have shifted between the two images. We first suggest a pre-selection of candidate pixels issued from the target image by keeping only those samples appearing to be lying in a region of the input space not yet covered by the existing ground truth (source domain pixels). Then, exploiting a classifier integrating instance weights, active queries are performed on the target image. As the inclusion to the training set of the samples progresses, the weights associated with the training pixels are updated using different criteria according to their origin (source or target domain). Experiments on a pair of QuickBird images of urban scenes prove the validity of the proposed approach if compared to existing benchmark methods.
Starting Page	3716
Ending Page	3719
File Size	311232
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6050032
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines Remote sensing Adaptation models Accuracy Knowledge transfer Context TrAdaBoost image classification domain adaptation domain separation active learning
Content Type	Text
Resource Type	Article

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