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Hash functions for near duplicate image retrieval

Auclair, Adrien; Vincent, Nicole; Cohen, Laurent D. (2009), Hash functions for near duplicate image retrieval, Workshop on Applications of Computer Vision (WACV), 2009 - proceedings, IEEE, p. 1-6. http://dx.doi.org/10.1109/WACV.2009.5403104

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WACV09_retrieval.pdf (1023.Kb)
Type
Communication / Conférence
Date
2009
Conference title
Workshop on Applications of Computer Vision (WACV), 2009
Conference date
2009-12
Conference city
Snowbird
Conference country
États-Unis
Book title
Workshop on Applications of Computer Vision (WACV), 2009 - proceedings
Publisher
IEEE
ISBN
978-1-4244-5497-6
Pages
1-6
Publication identifier
http://dx.doi.org/10.1109/WACV.2009.5403104
Metadata
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Author(s)
Auclair, Adrien
Vincent, Nicole
Cohen, Laurent D.
Abstract (EN)
This paper proposes new hash functions for indexing local image descriptors. These functions are first applied and evaluated as a range neighbor algorithm. We show that it obtains similar results as several state of the art algorithms. In the context of near duplicate image retrieval, we integrated the proposed hash functions within a bag of words approach. Because most of the other methods use a kmeans-based vocabulary, they require an off-line learning stage and highest performance is obtained when the vocabulary is learned on the searched database. For application where images are often added or removed from the searched dataset, the learning stage must be repeated regularly in order to keep high recalls. We show that our hash functions in a bag of words approach has similar recalls as bag of words with kmeans vocabulary learned on the searched dataset, but our method does not require any learning stage. It is thus very well adapted to near duplicate image retrieval applications where the dataset evolves regularly as there is no need to update the vocabulary to guarantee the best performance.
Subjects / Keywords
image retrieval

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