Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Conference paper
Title: A comparative study on the use of an ensemble of feature extractors for the automatic design of local image descriptors
Author: Carneiro, G.
Citation: ICPR 2010: 20th International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 August 2010: pp.3356-3359
Publisher: IEEE computer society
Publisher Place: Online
Issue Date: 2010
ISBN: 9781424475421
ISSN: 1051-4651
Conference Name: International Conference on Pattern Recognition (20th : 2010 : Istanbul, Turkey)
Statement of
Gustavo Carneiro
Abstract: The use of an ensemble of feature spaces trained with distance metric learning methods has been empirically shown to be useful for the task of automatically designing local image descriptors. In this paper, we present a quantitative analysis which shows that in general, nonlinear distance metric learning methods provide better results than linear methods for automatically designing local image descriptors. In addition, we show that the learned feature spaces present better results than state of- the-art hand designed features in benchmark quantitative comparisons. We discuss the results and suggest relevant problems for further investigation.
Rights: © 2010 IEEE
RMID: 0020114360
DOI: 10.1109/ICPR.2010.819
Appears in Collections:Computer Science publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.