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Detector adaptation by maximising agreement between independent data sources
Ó Conaire, Ciarán; O'Connor, Noel E.; Smeaton, Alan F.
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.
Keyword(s): Detectors; Information retrieval; dynamic programming; image classification; image resolution; object detection
Publication Date:
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Ó Conaire, Ciarán, O'Connor, Noel E. ORCID: 0000-0002-4033-9135 <> and Smeaton, Alan F. ORCID: 0000-0003-1028-8389 <> (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.
Publisher(s): Institute of Electrical and Electronics Engineers
File Format(s): application/pdf
Related Link(s):,
First Indexed: 2009-11-05 02:00:42 Last Updated: 2019-02-09 07:03:27