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Type: Conference paper
Title: The distinctiveness, detectability, and robustness of local image features
Author: Carneiro, G.
Jepson, A.
Citation: Proceedings, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2 / C. Schmid, S. Soatto, and C. Tomasi (eds.): pp.296-301
Publisher: IEEE
Publisher Place: USA
Issue Date: 2005
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 0769523722
ISSN: 1063-6919
Conference Name: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (18th : 2005 : San Diego, CA, U.S.A.)
Statement of
Gustavo Carneiro, Allan D. Jepson
Abstract: We introduce a new method that characterizes typical local image features (e.g., SIFT, phase feature) in terms of their distinctiveness, detectability, and robustness to image deformations. This is useful for the task of classifying local image features in terms of those three properties. The importance of this classification process for a recognition system using local features is as follows: a) reduce the recognition time due to a smaller number of features present in the test image and in the database of model features; b) improve the recognition accuracy since only the most useful features for the recognition task are kept in the model database; and c) increase the scalability of the recognition system given the smaller number of features per model. A discriminant classifier is trained to select well behaved feature points. A regression network is then trained to provide quantitative models of the detection distributions for each selected feature point. It is important to note that both the classifier and the regression network use image data alone as their input. Experimental results show that the use of these trained networks not only improves the performance of our recognition system, but it also significantly reduces the computation time for the recognition process.
Rights: Copyright © 2005 by The Institute of Electrical and Electronics Engineers, Inc.
RMID: 0020114257
DOI: 10.1109/CVPR.2005.340
Appears in Collections:Computer Science publications

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