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Online learning for parameter selection in large scale image search

Aly, Mohamed (2010) Online learning for parameter selection in large scale image search. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops. IEEE , Piscataway, NJ, pp. 35-42. ISBN 978-1-4244-7030-3. https://resolver.caltech.edu/CaltechAUTHORS:20170314-165620992

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Abstract

We explore using online learning for selecting the best parameters of Bag of Words systems when searching large scale image collections. We study two algorithms for no regret online learning: Hedge algorithm that works in the full information setting, and Exp3 that works in the bandit setting. We use these algorithms for parameter selection in two scenarios: (a) using a training set to obtain weights for the different parameters, then either choosing the parameter setting with maximum weight or combining their results with weighted majority vote; (b) working fully online by selecting a parameter combination at every time step. We demonstrate the usefulness of online learning using experiments on four different real world datasets.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1109/CVPRW.2010.5543758DOIArticle
http://ieeexplore.ieee.org/document/5543758/PublisherArticle
Additional Information:© 2010 IEEE. This research was supported by ONR grant N00173-09-C-4005.
Funders:
Funding AgencyGrant Number
Office of Naval Research (ONR)N00173-09-C-4005
Record Number:CaltechAUTHORS:20170314-165620992
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170314-165620992
Official Citation:M. Aly, "Online learning for parameter selection in large scale image search," 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, San Francisco, CA, 2010, pp. 35-42. doi: 10.1109/CVPRW.2010.5543758
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:75124
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:16 Mar 2017 17:26
Last Modified:03 Oct 2019 16:46

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