NDLI: Locality in Generic Instance Search from One Example

Content Provider	IEEE Xplore Digital Library
Author	Ran Tao Gavves, E. Snoek, C.G.M. Smeulders, A.W.M.
Copyright Year	2014
Description	Author affiliation: Inf. Inst., Univ. of Amsterdam, Amsterdam, Netherlands (Ran Tao; Gavves, E.; Snoek, C.G.M.; Smeulders, A.W.M.)
Abstract	This paper aims for generic instance search from a single example. Where the state-of-the-art relies on global image representation for the search, we proceed by including locality at all steps of the method. As the first novelty, we consider many boxes per database image as candidate targets to search locally in the picture using an efficient point-indexed representation. The same representation allows, as the second novelty, the application of very large vocabularies in the powerful Fisher vector and VLAD to search locally in the feature space. As the third novelty we propose an exponential similarity function to further emphasize locality in the feature space. Locality is advantageous in instance search as it will rest on the matching unique details. We demonstrate a substantial increase in generic instance search performance from one example on three standard datasets with buildings, logos, and scenes from 0.443 to 0.620 in mAP.
Starting Page	2099
Ending Page	2106
File Size	691407
Page Count	8
File Format	PDF
ISBN	9781479951185
ISSN	10636919
DOI	10.1109/CVPR.2014.269
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Vocabulary Buildings Visualization Search problems Databases Memory management
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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