DocumentCode
3256083
Title
Optimal Online Data Sampling or How to Hire the Best Secretaries
Author
Girdhar, Yogesh ; Dudek, Gregory
Author_Institution
McGill Univ., Montreal, QC, Canada
fYear
2009
fDate
25-27 May 2009
Firstpage
292
Lastpage
298
Abstract
The problem of online sampling of data, can be seen as a generalization of the classical secretary problem. The goal is to maximize the probability of picking the k highest scoring samples in our data, making the decision to select or reject a sample online. We present a new and simple online algorithm to optimally make this selection. We then apply this algorithm to a sequence of images taken by a mobile robot, with the goal of identifying the most interesting and informative images.
Keywords
decision support systems; information retrieval; robot vision; sampling methods; classical secretary problem; data picking probability; mobile robot images; online data sampling; online selection algorithm; Computer vision; Face detection; Image reconstruction; Image sampling; Image sensors; Mobile robots; Robot sensing systems; Robot vision systems; Sampling methods; Streaming media; GD-secretary problem; computer vision; optimal online sampling; secretary problem; sensor placement;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Robot Vision, 2009. CRV '09. Canadian Conference on
Conference_Location
Kelowna, BC
Print_ISBN
978-0-7695-3651-4
Type
conf
DOI
10.1109/CRV.2009.30
Filename
5230507
Link To Document