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Detector adaptation by maximising agreement between independent data sources

Ó Conaire, Ciarán and O'Connor, Noel E. and Smeaton, Alan F. (2007) Detector adaptation by maximising agreement between independent data sources. In: OTCBVS 2007 - IEEE International Workshop on Object Tracking and Classification Beyond the Visible Spectrum, 22 June 2007, Minneapolis, MN, USA.

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Abstract

Traditional methods for creating classifiers have two main disadvantages. Firstly, it is time consuming to acquire, or manually annotate, the training collection. Secondly, the data on which the classifier is trained may be over-generalised or too specific. This paper presents our investigations into overcoming both of these drawbacks simultaneously, by providing example applications where two data sources train each other. This removes both the need for supervised annotation or feedback, and allows rapid adaptation of the classifier to different data. Two applications are presented: one using thermal infrared and visual imagery to robustly learn changing skin models, and another using changes in saturation and luminance to learn shadow appearance parameters.

Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Additional Information:Workshop held in conjunction with CVPR '07. IEEE Conference on Computer Vision and Pattern Recognition.
Uncontrolled Keywords:dynamic programming; image classification; image resolution; object detection;
Subjects:Physical Sciences > Detectors
Computer Science > Information retrieval
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
Publisher:Institute of Electrical and Electronics Engineers
Official URL:http://dx.doi.org/10.1109/CVPR.2007.383448
Copyright Information:Copyright © 2007 IEEE. Reprinted from CVPR '07. IEEE Conference on Computer Vision and Pattern Recognition, 2007. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
ID Code:437
Deposited On:20 May 2008 by DORAS Administrator. Last Modified 04 Feb 2009 17:31

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