NDLI: Exemplar based metric learning for robust visual localization

Content Provider	IEEE Xplore Digital Library
Author	Le Barz, C. Thome, N. Cord, M. Herbin, S. Sanfourche, M.
Copyright Year	2015
Description	Author affiliation: French Aerosp. Lab., ONERA, Palaiseau, France (Herbin, S.; Sanfourche, M.) \|\| Sorbonne Univ., Paris, France (Thome, N.; Cord, M.) \|\| Theresis, Thales, Palaiseau, France (Le Barz, C.)
Abstract	This paper presents an exemplar based metric learning framework dedicated to robust visual localization in complex scenes, e.g. street images. The proposed framework learns off-line a specific (local) metric for each image of the database, so that the distance between a database image and a query image representing the same scene is smaller than the distance between the current image and other images of the database. To achieve this goal, we generate geometric and photometric transformations as proxies for query images. From the generated constraints, the learning problem is cast as a convex optimization problem over the cone of positive semi-definite matrices, which is efficiently solved using a projected gradient descent scheme. Successful experiments, conducted using a freely available geo-referenced image database, reveal that the proposed method significantly improves results over the metric in the input space, while being as efficient at test time. In addition, we show that the model learns discriminating features for the localization task, and is able to gain invariance to meaningful transformations.
Starting Page	4342
Ending Page	4346
File Size	2229979
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351626
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Visualization Image retrieval Robustness Feature extraction place recognition content-based image retrieval supervised metric learning visual localization
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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