Approximate nearest neighbors using sparse representations

Joaquin Zepeda 1 Ewa Kijak 2, * Christine Guillemot 1
* Auteur correspondant
1 TEMICS - Digital image processing, modeling and communication
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
2 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : A new method is introduced that makes use of sparse image representations to search for approximate nearest neighbors (ANN) under the normalized inner-product distance. The approach relies on the construction of a new sparse vector designed to approximate the normalized inner-product between underlying signal vectors. The resulting ANN search algorithm shows significant improvement compared to querying with the original sparse vectors. The system makes use of a proposed transform that succeeds in uniformly distributing the input dataset on the unit sphere while preserving relative angular distances.
Type de document :
Communication dans un congrès
IEEE. IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP'10, Mar 2010, Dallas, TX, United States. 2010, 〈http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5496145〉. 〈10.1109/ICASSP.2010.5496145〉
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https://hal.inria.fr/inria-00561778
Contributeur : Patrick Gros <>
Soumis le : mardi 1 février 2011 - 18:25:08
Dernière modification le : jeudi 11 janvier 2018 - 06:20:10

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Joaquin Zepeda, Ewa Kijak, Christine Guillemot. Approximate nearest neighbors using sparse representations. IEEE. IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP'10, Mar 2010, Dallas, TX, United States. 2010, 〈http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5496145〉. 〈10.1109/ICASSP.2010.5496145〉. 〈inria-00561778〉

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