Multiple Measurements and Joint Dimensionality Reduction for Large Scale Image Search with Short Vectors

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00235499" target="_blank" >RIV/68407700:21230/15:00235499 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Multiple Measurements and Joint Dimensionality Reduction for Large Scale Image Search with Short Vectors
Original language description
This paper addresses the construction of a short-vector (128D) image representa tion for large-scale image and particular object retrieval. In particular, the method of join t dimensionality reduction of multiple vocabularies is considered. We study a variety of voca bulary generation techniques: different k-means initializations, different descriptor transfo rmations, different measurement regions for descriptor extraction. Our extensive evaluation s hows that different combinations of vocabularies, each partitioning the descriptor space in a different yet complementary manner, results in a significant performance improvement, which exceeds the state-of-the-art.
Czech name
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Czech description
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Project
<a href="/en/project/LL1303" target="_blank" >LL1303: Large Scale Category Retrieval</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
ICMR 2015: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval
ISBN
978-1-4503-3274-3
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
587-590
Publisher name
ACM
Place of publication
New York
Event location
Shanghai
Event date
Jun 23, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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