DocumentCode
3564489
Title
Machine learning approach to fusion of high and low resolution imagery for improved target classification
Author
Ilin, Roman
Author_Institution
Sensors Directorate, Air Force Res. Lab., Wright-Patterson AFB, OH, USA
fYear
2014
Firstpage
195
Lastpage
199
Abstract
This work utilizes high resolution images in order to improve the classification accuracy on low resolution images. The approach is based on the machine learning paradigm called LUPI - “Learning Using Privileged Information”. In this contribution, the LUPI paradigm is demonstrated on images from the Caltech 101 dataset.
Keywords
image classification; image fusion; learning (artificial intelligence); high resolution image; image fusion; learning using privileged information; low resolution image; machine learning; target classification; Accuracy; Data integration; Feature extraction; Image resolution; Machine learning algorithms; Support vector machines; Training; Clustering; LUPI; Object Classification; SVM+;
fLanguage
English
Publisher
ieee
Conference_Titel
Aerospace and Electronics Conference, NAECON 2014 - IEEE National
Print_ISBN
978-1-4799-4690-7
Type
conf
DOI
10.1109/NAECON.2014.7045802
Filename
7045802
Link To Document