Type

Conference Proceedings

Authors

Alan F. Smeaton
Noel E. O'Connor
Ciarán Ó Conaire

Subjects

Computer Science

Topics
thermal infrared image classification image resolution object detection detectors dynamic programming information retrieval sources

Detector adaptation by maximising agreement between independent data sources (2007)

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.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> Subject = Computer Science
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: Centre for Digital Video Processing (CDVP)
Ireland -> Dublin City University -> Subject = Physical Sciences: Detectors
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Physical Sciences
Ireland -> Dublin City University -> Subject = Computer Science: Information retrieval
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres

Full list of authors on original publication

Alan F. Smeaton, Noel E. O'Connor, Ciarán Ó Conaire

Experts in our system

1
Alan F. Smeaton
Dublin City University
Total Publications: 450
 
2
Noel E. O'Connor
Dublin City University
Total Publications: 420